{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18d24d26-eaa2-43a8-bb51-7885f0c79e02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:16:40.583595Z",
     "start_time": "2023-08-19T14:16:39.195748Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:08:58.777887Z",
     "iopub.status.busy": "2023-08-25T16:08:58.776383Z",
     "iopub.status.idle": "2023-08-25T16:08:58.788677Z",
     "shell.execute_reply": "2023-08-25T16:08:58.788677Z",
     "shell.execute_reply.started": "2023-08-25T16:08:58.777887Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#导入需要的库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ad3553f-dc98-408e-a0cf-50b7cce584b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:16:43.575112Z",
     "start_time": "2023-08-19T14:16:40.585645Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:08:59.157652Z",
     "iopub.status.busy": "2023-08-25T16:08:59.157652Z",
     "iopub.status.idle": "2023-08-25T16:09:02.024237Z",
     "shell.execute_reply": "2023-08-25T16:09:02.024237Z",
     "shell.execute_reply.started": "2023-08-25T16:08:59.157652Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#数据farming导入\n",
    "df = pd.read_csv(r\"F:\\大三上\\数据分析与数据挖掘\\06\\小课\\数据源\\farming2.csv\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20b8a675-b4a1-419f-a0da-41b60fc81978",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:16:43.591282Z",
     "start_time": "2023-08-19T14:16:43.576683Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:02.025237Z",
     "iopub.status.busy": "2023-08-25T16:09:02.025237Z",
     "iopub.status.idle": "2023-08-25T16:09:02.039861Z",
     "shell.execute_reply": "2023-08-25T16:09:02.039861Z",
     "shell.execute_reply.started": "2023-08-25T16:09:02.025237Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#设置中文格式\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "262c5b6c-b609-4b7e-9087-9061c8b4e407",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:16:49.281377Z",
     "start_time": "2023-08-19T14:16:49.270651Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T13:30:31.021451Z",
     "iopub.status.busy": "2023-08-25T13:30:31.021451Z",
     "iopub.status.idle": "2023-08-25T13:30:31.051386Z",
     "shell.execute_reply": "2023-08-25T13:30:31.051386Z",
     "shell.execute_reply.started": "2023-08-25T13:30:31.021451Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2103048 entries, 0 to 2103047\n",
      "Data columns (total 13 columns):\n",
      " #   Column     Dtype \n",
      "---  ------     ----- \n",
      " 0   农产品市场所在省份  object\n",
      " 1   市场名称映射值    object\n",
      " 2   农产品类别      object\n",
      " 3   农产品名称映射值   object\n",
      " 4   规格         object\n",
      " 5   区域         object\n",
      " 6   颜色         object\n",
      " 7   单位         object\n",
      " 8   最低交易价格     object\n",
      " 9   平均交易价格     object\n",
      " 10  最高交易价格     object\n",
      " 11  数据入库时间     object\n",
      " 12  数据发布时间     object\n",
      "dtypes: object(13)\n",
      "memory usage: 208.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#检查数据\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac60e454-2959-466f-89e2-dfd8ca6af775",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:18:11.944753Z",
     "start_time": "2023-08-19T14:18:10.017521Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T13:30:43.780366Z",
     "iopub.status.busy": "2023-08-25T13:30:43.780366Z",
     "iopub.status.idle": "2023-08-25T13:30:45.738950Z",
     "shell.execute_reply": "2023-08-25T13:30:45.738950Z",
     "shell.execute_reply.started": "2023-08-25T13:30:43.780366Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "农产品市场所在省份    0\n",
       "市场名称映射值      0\n",
       "农产品类别        0\n",
       "农产品名称映射值     0\n",
       "规格           0\n",
       "区域           0\n",
       "颜色           0\n",
       "单位           0\n",
       "最低交易价格       0\n",
       "平均交易价格       0\n",
       "最高交易价格       0\n",
       "数据入库时间       0\n",
       "数据发布时间       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4174478-c6ae-4b0a-bee5-15a4297374b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:18:14.231095Z",
     "start_time": "2023-08-19T14:18:11.946755Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T13:30:45.739952Z",
     "iopub.status.busy": "2023-08-25T13:30:45.739952Z",
     "iopub.status.idle": "2023-08-25T13:30:48.273705Z",
     "shell.execute_reply": "2023-08-25T13:30:48.273705Z",
     "shell.execute_reply.started": "2023-08-25T13:30:45.739952Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Q/Pz8NHfuXEnS/PnzZa2Vn5+fHn/8cb300kvKnj17mlGckHWlfrhVp04dWWvVpUsX59zVt29fNW7cWHPmzHHeJho6dKiKFi0qKWWaBWut3nzzTUlSXFycnnvuOWd+3NTnNh6kuUNsbKwz1O9TTz3lXNOWLFnibFO7dm1FR0crPj5eFy5cUNWqVRUeHq7x48dLkqZMmaK7775bOXLkkLVW3bt31549ezRz5kxFRUWpdu3aSkpKoqZcavbs2bLWOqNK7N+/X+3atVNAQIAefvhh+fv7q23bttq2bZskaeTIkfLz8/PpIJBSrpmvvPKKQkJCtHDhwht+HMhc3iBOjx49ZK11phCKj493RsxZvny5IiIi1LhxY2dkCa/z58+rV69estaqU6dOzmgUP/zwg/z9/VWkSBH98MMPN/CI8E8wffp0Z6j7pUuX6sKFC3rhhRc0efJklS9fXtWqVVNcXJwkae3atQoPD3eeN3zyySey1uq1117TwoULlS9fPhUuXFiTJ0+mU9LFqClkNGoKGe3IkSPas2ePPvzwQ/Xo0UOzZ89WUlKSvvjiC5UpU0bVqlXz2X7GjBnKnj276tWrp2zZsilfvnw+Uyzs2bNHffr0UUBAgJo0acKIzi5ETQHpIziBa7J+/XqFh4fr7rvv9plXe+jQobLWqkyZMsqbN6/at2+vqKgoVapUSb/99ptPG4cOHdJ//vMfWWv11ltv3ehDwD/IyZMnnVEjvNMhSCnTv1SvXl3WWo0cOVKNGzf2CU143+L2PpTt0qWLrLWqV6+eNm7cqNGjRysmJkZTp07NjMNCJlm7dq1uv/125cyZU++9957zJtqkSZNUrlw5RUVFaevWrc72Fy5cULt27dStWzclJiZq48aNqlChgqy1GjVqlLPdZ599JmutSpcu7XSKw31atWolj8ejYcOGOaOZJCcn67ffflO9evVkrXVGLBk+fLhPaOLrr79WQECARo8erXfffddJcj/11FOZdjzIHKkDfd7rnJ+fnxPYunTpkvMAbP/+/brppps0btw4Z3qO1PdN3o6ohx9+WNmyZVOrVq30yy+/3MCjwT/BkSNHNHToUPn7+6tFixaSUu7XP/74YyewlT9/fn322WeSpNOnT+v+++9Xjhw5NGzYMCUkJGjDhg0aMWKEsmXLJmutPB6Pc55q27ZtZh4eMtmCBQtkrdXcuXN1/PhxtWnTRoGBgfriiy90+fJlJ/j1zDPPqEuXLj7XPu85yns/9sMPP8ham6ZTHO4xZ84cWWvl7++fZoSIBQsWqEqVKgoJCXG+1/344486duyYJOno0aMqWbKkIiIitHHjRv34448KDAxU+fLl9dVXX2XG4eAfwBsqvfPOO9WuXTtZazVo0CC9+eabmj9/vhNE/fTTTxUSEqLjx49r1qxZPkHUhIQEff311ypQoICKFy+uiRMn0inpYtQUMho1hYyUeiozr/j4eD300EOy1vqMXjljxgxly5bN+T73yy+/KDg4WAULFtS4ceOc7Y4cOaIWLVrIWqt33333+h8E/lGoKSB9BCdwzVq1aiV/f3+1atVK69evl5TSKeSda9v70H7w4MHyeDzq2LGjjh8/7tPGli1bVKRIEQUHBzujBMCdvv/+e1lr1bFjR2eYaUlas2aNnnjiCfn7+8vf3z/N0Pep3+IOCgrSE088oTx58qh48eLKkSOHmjZtmhmHg0y2bds2lStXTpGRkRo/frw++OADVahQQeHh4erevbvz5fLChQt65JFHZK1VSEiIMw/8unXrVKtWLX3yySdOm1988YX8/PxUtmxZnxqFO3iHjJ40aZKstSpatGia8MSWLVv04IMPKkeOHKpXr54CAgKcN2q9oQlvjb399tsKDAzUc8895/wM5uXOmlI/xLryd3z58mVn+rLs2bNr4MCB+uWXXzR27Fh9/fXXOnv2rGbOnKm77rpLvXv3dqb2SP15KWX41sDAQFWqVEnWWjVu3Fjx8fE35gCRaVIPZS+ldCgOHjzYGRKzbNmyqlGjhhISEvT5558rZ86cKliwoDNi3Pnz55UzZ07FxMQ4b7kdOnRIUVFRatSokcaMGaOBAwdq3LhxWrZs2Q0/Ptw4f/WwfcmSJbLWasSIEXrhhRcUGBioOXPmOOu9owl6AzfekSm857zU7Y8aNUpBQUGaP3/+dTgS/Fs0adJE1lrlzZtXixYt8ll37tw5rVq1SjVq1HCm9WjdurUziuWCBQsUEhKie+65RwEBAbr11lud74QSnUdukvq+6v3333fCfu3bt0+z7bFjx1SxYkX16dNHS5culZ+fn95++21JvjXjfRmoXLlyeuONN5zQF9yBmkJGo6ZwPX3xxRfatWuX8/cdO3aodOnSCg8P18CBAzVz5kyngzsuLs6px7lz58paq5w5c/pMr96mTRtZa9WjR4803zXhDtQU4IvgBK6a94Q4YcIEWWuVK1cuNW/eXHv27NG3334ra63GjBnj85nmzZsrODjYeUCW+mF+2bJlVb58eabrcLHk5GQdPHhQRYoUcYZt9b5VJEmrV69Wy5YtZa3VG2+8kSY04e2QHDVqlJKTk9W8eXMnhOFFh6T7bN++XeXKlVNwcLBy5cqlqKgoPfnkkypbtqxefvllHThwQI888oiCg4M1dOhQNWrUSAEBAU6a/8yZM05bq1ev1l133SWPx+MMmYis688euB84cEB58+aVtVYFChTQ8OHDnQ5H78gT3ukXnnnmGUkpD/hThybeeOMNpxPAi3NU1pTe79X7ZfHo0aPq1KmTAgMDdf/99yt79uwqUKCAypYtK2utbrrpJvXs2VMVK1ZUmTJl9NRTT8laqxdffFE7d+502luwYIE8Ho8GDRqk48ePq1evXtq0adMNO0Zkju+//17vvvuuz7XK64UXXpCfn5+KFSummTNnOstnzZql8PBwFShQQDNmzNDbb78tj8ej77//XufPn3fmS65YsaLuvffedKc6o0My60l9nlq8eLFPONT7+548ebKstapataqsterVq1eadvr16yePx+NMlZBeaGLGjBmqWLGi7rzzTkbvcilvXYwbN05+fn6y1ipPnjxO2Eb63xtvx48f1+zZs1WjRg1ZaxUcHOyc0+rXr+8Me/711187n+Uc5U5HjhzR2LFj5e/vL2ut6tatq8mTJ+vRRx/Vyy+/rAULFmjlypUqV66cOnTokGYkJe/1bvHixQoICFCrVq0UHBysEiVKEJh3KWoKGY2awv9Hes8V5s6dq5CQEN1///3OlGeStHv3bt1yyy0KCAhQUFCQ2rVrp/j4eKeNAwcO6LXXXpO1Vq+//rrzuTfeeEPh4eHy9/fX77//ft2PCZmLmgKuDsEJXLN9+/YpNDTUebOoadOm+uSTT9StWzdn6FVv50BsbKyKFSum++67z6eNhx9+2JlHmTcj3eHPOgc7d+7spK+HDh2qkydPOuvWrl2rRo0aOQ/YrgxNpH6LOyIiwie9TYdk1vdHD0l/++03lShRQgEBAVqzZo127type+65R9mzZ1e5cuWULVs2jRkzRidOnNDp06fVoEEDZc+eXR999JEzVGJycrKOHTumIUOG6MUXX9S5c+du5KHhBvI+rJDSvsntXXbu3DlVrVpV2bJlU9GiRZUtW7Y04YnNmzerVq1aioiI0DPPPKOgoCBn2hdCE+6R+vdap04dn995cnKyDh06pIIFC6pfv36KjY11AhMxMTF65plnNGzYMC1fvlyTJk3Szp07tX37drVs2VJ+fn7q2LGj4uPj9f3338vj8TjTw1z5c5E1eafeqF+/vg4ePOiz7rffftMtt9yicuXKyd/fX+3atdO5c+ecupg1a5YiIyNlrVVgYKC+/PJLSdL48eNVqFAh7dmzR1WrVtWtt95KqNkFUp8vnnnmGVWsWDHdkSBefPFFWWvVoUMHFSpUSKVKldKXX36pixcvSvrf1FTffvutT7tXhiZuu+02FStWzGfKNGRNfxVgiI2NVZ48eeTxeFS8eHFFR0c70y8mJyenuQ/r37+/ihQpotWrV2vDhg0KCAhQ5cqVNXv27Kv+mciazpw5o06dOslaq5YtW6p06dLy9/dXyZIlnbBg69atVbduXcXExOjtt99WVFSUbrvtNn399dfOdz5vEHXw4MGSpIULF2rbtm2ZeWjIJNQUMho1hb9j//79PiNqXTmVwv79+9WqVStZa1WjRg2fUb53796tW2+9VdZa9ezZ01l+4MABvfLKK7LWOnXktWbNGg0YMEDTp0+/TkeEzEZNAdeO4ASuSUJCgk6dOqWiRYuqXr16at++vay1atq0qTNth/S/hxcJCQnq3LmzAgMD9dNPP0mSfv/9dz3zzDOqXLmyDhw4kCnHgRtn//79zv9f2anj/fvEiROded6ttXr55Zd16tQpZ7s1a9Y4F/B33nknTWiCDkn3+P3337Vx40Zt2bLlL7fdsGGDz5fJX375RVFRUfJ4PGrfvr1POn/Hjh1Oh7f3QWzqh7DpvX2LrGHnzp3Knj27AgICnA6dP5ovtHPnzgoODtZXX32lRo0apRue+O2331SrVi1Za/Xwww/r4sWLGjduHOcol0j9e73nnntkrVVERESat7RjY2OdjscZM2Y4byDly5dP/fv3TzPs6rZt2/TUU0/Jz89P9evXV2BgoIYMGSKJDiO3mDdvnvz8/PTQQw9p6dKlzvLk5GTFx8erQ4cOql+/vj777DONGDFC/v7+atu2rU8YderUqfJ4PPruu+8kpYx+UqhQIT366KOSpPLly6tkyZI+92DIelKfp3r06KHixYurSZMmzrXMu82lS5f0+OOPK3v27Jo3b55mzpypm2++WcWKFdPXX3+tIUOG+IwE91ehiau5d8O/V+rzxh9dlxITE3X58mXVq1dPuXLlUpcuXXTTTTcpT548PuGJ5ORknzo9f/681q1bp6CgIN1yyy2MNAHH888/r5deekmS1LVrVycc2LhxY40cOVI7d+7Upk2bFBcXpwsXLmjixImKjo5W6dKltXLlSs2bN08ej0cDBw7M5CPBPwU1hYxGTeFaXLhwQf/5z38UGhqqESNG+NwPHT582Am4Hzp0SM8995ystapevbpPR/eePXt0yy23KDQ0VEOGDFFsbKyGDBkia63zDEHyfWnowoULPKPKoqgp4O8hOIE0kpKSdObMGR08eFCrV6/W9u3b07x59sgjjyh//vxKTEx0bvyaNGmi3377zacdKWVIYWutJk2a5NMGb7Nlfd6hnt59911nWXpvc2/dulVhYWGqWrWqnn32WVlr1bdvX58HcGvXrtXjjz/ujExBaMKdRo8eraCgIL388svOstWrV2vRokV/OmfapUuXVK9ePfn5+SkiIkKFCxd2poXxvt22efNm3X777YqOjqbTyGXGjBmjmJgYRUZGOtexs2fPavbs2c7Q45KcNPVrr72mI0eOqEaNGgoODvYJTyQlJWnTpk2qX7++cubMqUaNGikiIoJzlAuk/r1WqVJFefLk0ejRo1W9enVFRkaqT58+6W7722+/6a677lLnzp1Vs2ZNWWvVv39/n6mrpJSAV7t27WSt1Z133ukzbQeyNm9oombNmvr555+d5ak7DHfu3KkVK1ZISunAHDp0qPz9/fXUU0/51JJ3moSLFy9qxIgRypMnj2bNmqVz586pRYsWatWq1Q06KmSG1DXz0ksvqVixYnr++ed15MgRSfIJ2iQnJ2vx4sX6/vvvJaV8d/vkk09UoEABhYWFyePxOCOXeO/BCE2407x581S5cmV9+OGHzrL4+HifoX5TGzlypKy1+vnnnzV79mzly5cvTXjCG96RpB9//FGBgYEqV66cz9tyhCYgyamTAwcO6JZbblF4eLj8/PxUo0YNn7m2pZT7+ylTpihPnjzKnz+/AgIC1L9/f0nUE/6HmkJGo6ZwLebOnauwsDDlzp1bb7/9tqSUvpVmzZpp+vTpzos+sbGxev7559Pt6N69e7fKlSvnPGu/clQAnkm5CzUFXDuCE/Dx6quvqkqVKipWrJjy5MnjzD165cOuHj16qFSpUtqzZ48k6T//+U+64QkppVMzdUd3em8jIWuaNWuWrLUqWrSoM42LlPLgNXWHz/79+5UnTx6VLl1ay5YtU9u2bdMNT3hHnvB4PBoxYoRef/11QhMu4T1ffPnll7LWavTo0ZJSAjU1atRQQECAcz660tGjR/Xggw8qJCREw4cP16hRo+Tn56d69eqlqZeGDRvKz89P69atu67Hg3+G1L//6dOnq1ChQsqdO7c2bdqkZcuWKTIyUg0aNHBGBtizZ49atmypNWvWSEqZr7RmzZppwhOStGLFCpUsWVLWWqYQcoErQxMRERF67733JElxcXGqUaOGcufO7ROeSB32euqpp1SsWDFdvnxZDRs2dMITV85bu3nzZmfu2+7duzu1iawr9UgTGzdudJZv3rxZ0h/fT8fFxWnYsGHy9/dXhw4ddPjwYd1xxx0aO3aspJQAxS233OIznV7q6yj36VlP6vNUz549FRkZqTJlyji1dPjwYfXt21ddunRJ8xlvPXjDE+XKlVOePHn0888/O2H41Oc0QhPuMmXKFFlr1a5dO0kpnT5NmjRRrVq1tH37dmc7bz19+OGHPnO4T5w4Mc3IE95tlyxZoqCgIN12222EJlzoymD8n/3ez507p8aNG6tu3bqaMGGCrLWqWrWqTwjau93HH3+s4sWLKzIyUnPmzGF0QRehppDRqClcD4sWLVLu3LmVO3duvfjii7r//vtlrXWm1vPW2V91dMfExPg8Q5V4JuVW1BRwbQhOQFLKyfHo0aOqXr26ihcvrocffliPPPKI2rRpo8GDB2vv3r3OdlLKjaH3jTWvPwpPbNq0SdZa9e7d+8YdEP4x5s+frxIlSqho0aLO3FYzZ85UtWrVnLfUEhIS1Lx5c5UtW9YJVfxReGLz5s1q0KCBrLUKCAggNOEyGzduVHBwsEaPHq2DBw+qWbNmCgkJ0eTJk9Pd/vLlyxo5cqQCAgI0cOBAZ47Ihx56SIGBgVq8eLGklHPb66+/rtDQUMXExPi8cYmsLfV5Y8aMGYqJiVF4eLjy5MmjmjVrOte61FNQpXZleOL06dNKTEzUiBEjFBoaqo4dO6b7s5B1pBea+OCDD3TmzBln+f79+9MNT3jfPlq4cKE8Ho8zVVDNmjXl5+eXbnhi3bp1atmyZZoHbMh6UocmVq1aJSnlHqp48eK65ZZb0kzpcqW4uDgNGTJE/v7+uvfee+XxeJx5Sb3T7XnvxVI/5KVDMmvr1q2bypUr57wp9Nlnnyk+Pl79+/dXqVKl5Ofn53SAS2k7BM6dO6dPPvlE+fPnV7FixTR79mzn/koiNOFGmzZt0k033aR8+fJpx44datq0qTNKYP369bVjx440n1m2bJlPGGzSpElpwhNr1qxReHi4SpYsSWjCRdavX+/UgFd6v/Ndu3alGe1m3rx5stZq7dq1mjt3rqy1qlatmpYtW+bz2dOnT+ujjz6Sx+PR3XffzXe/LI6aQkajpnA9eZ8vrFixQjlz5lRQUJCCg4M1c+ZMSWlHevuzju7t27c7L3SkbhvuQk0B147ghIutX79eS5Ys8Vl25swZbdmyRcuXL9e2bdvS/dyVD89SnyBThye8D8rGjRsna61efPHFDD4C/JOl/tKwYMEClSxZUkWKFFH37t2VN29eValSRUePHnW2OXLkiLZs2eJ8btu2bU54ol+/fk544sKFC+revbustXruueecz3OhzppWrVrldPJIKcEZPz8/Pfzww2rWrJlCQ0OdQI7XlR3bq1at0ueff+5Tb4sWLZK11pknMiEhQX369FGJEiWcjku4h/e6dvnyZfXq1UsBAQEKDAzUhg0bnOV/xhueyJYtm4YNG6aZM2fq9ttv15NPPulswzkq66tcubLy5s2rDz/80AlNrFmzxgl27dmzR9WrV1fu3LnThEnj4uKUO3dunzDgQw899IfhiXPnzl3fg0GmSy80IaWMtJQzZ07dfffdV9VOXFyc3nrrLfn7+ytHjhzO29+tW7dW48aNtXv37uux+/iHGjZsmCIjI9W+fXsdPnxY3bp1U2BgoJ5++mndfffd6tatm7p37678+fP7jJZ05fe/CxcuONN2FC9eXLNnz9bZs2f1+eefq3z58oQmXKhXr14KCgpS+fLlFRYWpjfeeEO9e/eWv7+/GjRo4DxbuLJjKfX9kTc8ER0dre+//15Hjx7Vs88+63NvTmgiazt79qyKFSum6OhoPf300/rss8/0888/a+XKldqwYYPmz5+vIUOGqFWrVgoNDdVPP/3kfDY5OVn79+9XgQIF1KFDB0n/Gw0lvU7JkydPasKECVq+fPkNPUbcWNQUMho1hesp9X3OihUrdOedd8rj8Sg8PFxTp0517puuHBXuzzq6vXgm5U7UFPD3EJxwqZ07dyp79uyqUqVKmhsw702b9w00STpx4kSazsjUUp8oX3jhBVlr9fjjj2vXrl06e/asvvnmmzRTeCDrS05Odi6433zzjW6++WYFBASoTJkyzjZ/VlepwxN9+/bVkSNHNHLkSIWEhDDShAskJCSoY8eOstaqWbNmzvKuXbvqjjvuUFhYmDPv9rlz57Rx40ZNmzZNn376aZq6Sv1WbnJysn777TdZa1W3bl2fm8gtW7ZQT1lY6t/1lV8OLly4oPnz5ytfvnwqU6aMYmJilDt3bm3dulVS2k6jKx05ckQPPvigQkND9corrzjzBqb+Gch6vDU1ZswY581ar99++82ZrsX7wGzPnj2qUaOGIiMjfUJhiYmJql27tqpXr+5zv5Q6PJE6/IWs7Y9CE5J07NgxFS5cWAUKFFBsbOxVtbd06VLlyZNHTz75pDOtgqR0H34ga/voo480aNAg7d+/X1JKuLlAgQKKjo7Wq6++KinlvNa1a1flzZv3qsITBQsWVGRkpMqXL688efKoePHihCZcxHuPvWzZMllrVaBAAX300UeSUt5Iq1Gjhqy1atiwYbojT0hpwxPR0dHKnTu3fvnlF5/tCE24w8svv+yMWOKdPvbK/wICAlSwYEEtXLgwzecff/xxRUdH6+zZs5KkTz75xOmU/PHHH322/av7e2QN1BQyGjWF6yH1fc7SpUv14IMPKkeOHBo+fLhzb5Tec6Zr6eiGu1BTwN9HcMKl9u3bpxdffFFBQUF66KGH9PPPPzvrHn/8cQUHB2vHjh1KTk7W5s2b1bJlS/33v//90zZTP/B47rnnZK1V5cqVeXDmYt6auHTpkn766SdFRkYqe/bsKlq0qD799NM023mlvrCnDk8MGTJE48ePV8uWLf/ws8haYmNjVbduXXk8HrVt21YbN27UCy+8oJw5c6px48aaNm2a6tatqxIlSiggIEDWWpUqVUoJCQk6fvy4M2/3lY4dO6bQ0FDnjd2/Gu4cWcvhw4c1e/Zs7dmzR1LKnO2zZ89Wvnz5VL16dSUkJGjWrFmKiYlRZGSk85bkXz20OHTokB588EEn7CVxjsqqrjxnbNy4UZ07d/YZyUaS3njjDUVGRip//vxav369pJTwhLdO+vfv72z722+/6cKFC0pMTPSpm7p168paqx49enCucoE/Ck0kJyc756BWrVrJz89PK1eu9PnslZ2K3r9/8MEHstbqww8/THc7uMPJkyf1xRdfaN++fZJSAn8DBw7Urbfe6oQm+vfvr1mzZikhIUHdunVLE564Mph64cIFzZo1S7fddpv8/f111113EZbP4latWqVZs2Zpz549ThDr/PnzatmypcLDwzVr1iydP39eZ8+e1dixYxUQEKDbb79dQUFBqlu3rn7//fd02019fXv//feVO3duWWvT7XBC1jZ79mxZazVgwADt2bNHy5Yt05IlS/TJJ5/o22+/1ffff69169almTrWe20bOnSo6tevr2XLljnLvJ2SDzzwgDOXN9yDmkJGo6aQ0VI/a1qyZIkeeOABhYWFadasWZKkH3/8Ublz51bu3Ln1zjvvONv+VUf3lTUI96CmgP8fghMuk/pB6ZEjR9SvXz95PB7VrVtX69atkySVKlVKDRs2lJQyH9vDDz/sJGa9w01fTfvNmzdXQECA0zGFrM37uz958qQOHTrkLI+Pj9ecOXOUL18+1axZU998841KlSqlIkWK+NTTn3Uubtu2Ta1atVJAQIB69OihI0eO/OVnkHXExsaqQYMGstaqSJEiCgsL0+jRo51zU3BwsKpUqaLHHntML7/8sj799FPFxcWpV69euu+++3zmT/Y6ceKEcuTIoVq1amXCEeFGW7JkiebNmycpZdj6oUOHqlChQs7ISAsXLlRkZKTuuecepxMgOTlZU6dOVaFCha4pPLF9+3bVq1fvD0M7+Pfbt2+fevbsqalTp/os37hxo7p06SJrrfr06eMsHzlypHLkyKF7773XqaNdu3apRYsWev3113XmzJl0O7JTTxHzyCOPaMaMGdfpiPBPkTo0kToUceWco4MHD5a11jmv/ZmLFy+qbdu2qlSpknOvT3DCHX766ScNHjxYR48eVWxsrPr37698+fJp3rx5SkxM1JQpUxQVFaW2bdtKSgl15cmTRzly5NCSJUsUHx+f7sgT6Tl16pRWrVqlY8eO3YhDQyY5ffq0c0/u7++vRx55RKdPn1arVq1krdVtt92mvXv3SpJGjRolPz8/vfjii0pISNArr7yioKAg1axZU9u2bXPOa97vdZJvMGfkyJEKDg7mfspFvNempKQklSpVSlWrVk1zvUpMTNSrr76qUaNGOVN5Xjml3h+FTL2dkvfcc89Vj9iEfzdqChmNmkJGu/L50uLFi3X//fcre/bsTge399n3Tz/9pKioqKvq6O7UqZOstapZs6YzVSPcgZoCMgbBCZfy3rSdOHFC/fv3d8ITr7zyikJCQjRt2jTt3r1b1apVc06edevWVUBAgD777LM/bdt7Uk1MTPTpQEfWdOjQIechV2xsrEaOHKnOnTtrw4YNunz5smbMmKH8+fOratWqunjxoqSUjoESJUqoaNGimjJlitPWnwUhtm7dqjp16igiIoK6ysIuX76svXv3auPGjVq/fr3z1uKiRYtUsGBBJ+EqSdOmTdPixYudB7ReZ8+e1cCBA2Wt1TPPPJPuz/nmm2+cGz6JEE5WlZycrJ9++knWWnXq1EmnT5/WgAEDZK1VyZIlFRAQoA4dOqhZs2Z67LHHnAcbBw8edNqYPn26Mwz51YYnGBUga1u6dKn8/f0VFRWlRYsWSZJ++OEHjRkzRt9991264Yk+ffooJCREbdq0UXx8vLP8woULPm1///33Ptc4b61xjsr6UocmVqxY4bMuISFBiYmJOnz4sHbs2OFc42rWrKl3331XAwcOVJ8+fdS3b1+NGDFCgwcP1rlz55zPx8bG6tixY9SRi1y6dEkjR46UtVZVqlRRy5YtFRkZqc6dOzvbbN68WQ8++KDy5cvnhEw///xzWWt1xx13aPPmzTp79qy6d+8ua60zF/efjRSHrG/58uV69913Vb9+fb3yyiuqX7++QkJCdM899yggIEB16tRxni88++yzzucWL16smJgYWWu1YcMGSdLu3btVrlw5Pf/88852qeuLIE7Wt3XrVq1Zs8ZnWXJysh599FFFRUWluac+efKkMxJXv379nGvdlQ/4r2zPa8qUKX/5PAv/btQUMho1hevlwoULev311/XWW29JSrnHKl26tIKDgzVz5kxJ/5sC+1o7ug8ePKjmzZvLWqt69erxjMolqCkg4xCccKEFCxaoSZMmzgOyuLg4DRw4UP7+/goNDdVNN92kRYsWqX79+oqMjHTeTuvfv7+stQoMDEzzliXcJzExUZ9//rluv/12nTx5UidPnnTegLTWqnXr1po7d65CQkJ05513Ol8WvJ1A33777TWHJ9atW+cML4ys6dSpUypTpoz8/PwUFBSkHDlyaPTo0erVq5cCAgIUHBysoKAgPf30006teAM5UkqQZ+jQocqZM6eee+45Z/mVQ0t/9913uv32232G1EfWtHv3btWqVUt58uTRCy+8ID8/P2dI8gceeEAxMTFq3769tm7dKinlQUWtWrU0ffp0p43PPvvsmqftQNb2/vvvKzo6WrfddpvGjh2rsmXLqlChQjpx4oT27dvnhCd69+7tfKZJkyYKCAhQly5d9NBDD+nNN9901p0/f15ff/21smfPrpdeekkSnZFusmDBAnk8njShiYkTJ6pXr16qU6eO7r77bsXExCg6Olrh4eHpzqWc+r85c+Y47aS+BnLuco+LFy9q8uTJCgoKkrVWzz//vF5++WVn+sVdu3Zp69atql27tvLkyeOMcjJ9+nRZa/X444/rzJkzzndF3ixytyuvScePH1e1atWUI0cOzZgxQ8nJyWrevLn8/Pzk5+enNm3aONtu3LhRzZo1U2BgoDP8+L59+5xQdNeuXX3aJ+TlDr/++qsKFy7sjHoj/e8aNWbMGFlr05365/DhwypfvryCgoJ8goJ/VjfcU7kDNYWMRk3herl06ZK6du0qa63y5cun9957T9OnT1f+/Pl9npGfPXs2zWeXLVt2VVMs7Nq1S4899pgTWEXWRk0BGYvghAu99957stZq2rRpzrILFy7o2WefVWBgoJ5++mk1atRIOXPmdOZWnjlzpqy1evbZZ/Xggw9e1cgTyNpOnjzpzOc+aNAgvfrqq87/v/vuu/Lz89OgQYM0fPhwHT9+XFLK25Tff/+98wD/akee4AuEexw4cEAjRoxQ9+7d1b17d9WqVUu33XabcuTIIWutatSooXr16slaq8cee8zns0eOHNHQoUMVGRmpF154wVme+u3u1MMdpneziKxp+vTpTkfj4MGDJaWMEBAdHa0cOXIoMDBQnTp10r59+/Txxx87nUx169bVli1bnDauddoOZD2pr0+TJk1Srly5FBISoptvvtm51knS3r17nfBEz549neWVKlWSn5+f7rjjDhUoUMB5E0BKCfnUrl1bAQEB2rFjx405IGS6ZcuWyc/PT6VKlfIZQenw4cOqXLmyE4SIiYlR6dKl9eCDD6pRo0ay1qpYsWIaMWKEhg8frrfeektvvPGGRowYoY4dO2r37t2SUq5177zzjj799NNMOkJklmPHjmncuHHKnj27xo0bp/Pnz+vuu+9WmzZt9NprrznTt+zevVs1atTwCU+8+eabstY6Aa+TJ09q/fr1mj9/vt577z0NGjToqqaKQdZ06tQptWzZUmFhYZo8ebLTIfT666/Lz8/PCd6cOHFC+/fv1xNPPCGPx6O5c+dKSglNFChQwAlNXDkdEdxhw4YNKlKkiPz9/bV8+XKfdR999JGstc49t5f37cYzZ86oYsWKCggI0KBBg66qUxJZHzWFjEZN4Xq4dOmS/vOf/yggIEDNmjVT9uzZVbRoUY0aNcr5DpeUlKT4+Hj16tVLU6ZM0erVq33aSD1KwLhx45zl3vry/nnlS2TImqgpIOMRnHAhbwjC+7ArMTFRycnJeuGFF5QjRw4FBAT4PDibMWOGrLV67bXXJKUMj+8deWLy5MmZdhzIfNu2bdPdd9+tXLlyOaEJKWU0CWut/Pz81L59e+3Zs0fbt29XiRIlFB4erscff9y5cC9cuPCaR56AO+zbt09t2rRRaGiovvnmG9WoUUPt2rXT4cOHVb16dWfowx07dig2NlaDBw9Wrly5fEaaSO2NN97QBx98cIOPApntxIkTznDl77//vqSU0FZUVJQz7Uu7du0UHh6uTp066eLFi9q4caP69u2rAgUKaO3atU5b06dPV9GiRRUZGanff/9dEg/53Wzr1q0qVKiQ/P39ddttt+mnn35y1h09elRDhw7VSy+95BOemDp1qsLCwlSzZk1VqVJFefLk8QlPtG3bVtZaffvttzf8eJA51q5d6wS7PvnkE0n/O69MmDBB06ZN04oVKxQfH+88iD127JhCQ0P15JNPptumdySms2fPOmGwhx9++AYcDf4pDh8+rBEjRigqKkpPP/20s3zYsGGy1srf319t27ZVXFycJGnPnj2qXr26oqOjneB88+bNFRYWpn379un1119PM6pJ1apVdfr06Uw5PmSexMREtWnTRtZade/eXVJKSPnVV1+Vx+NR586dnTmQq1Wrpocfflgej8cZBSf1SBO9evVyvvMRRnUn70s93nsh75SyEydOlLVWq1ev1uTJkzV27FjnM95r4dmzZ+mURBrUFDIaNYWMFB8fr+7du8vj8WjUqFGKj4/XrFmzFBgYqKJFi2rGjBnOtt4p9Lz37g888IBWrVqV7hQLb7/9dmYdEjIZNQVcHwQnXGjy5Mmy1urnn392lp08eVJlypSRtVaFCxd25m/zhiZGjRolKeVt8Fy5cqlmzZqqWrWq/P39NXXqVG76XCo+Pl7t2rXzeSNt/vz5ioqKUrVq1ZxRKJ577jn9+uuvWrlypTNaQOqHrQsWLFCJEiVUrFgxn/AE3CV1B/TatWvVpEkThYaGOlMDFS1aVE2bNpWUci5q27at8ufPr0KFCmnIkCHKmzev+vTp49PmwYMHNWXKFNWuXdt5O/fEiRM37qCQ6dauXasKFSrI399f69at06pVqxQVFaWqVas6nYsdOnSQtVbZsmXTs88+q6NHj0r63wgl3of5iYmJmjZtmjwejypVqsSoJS6VnJyspUuX6tZbb1WxYsX0+uuvK2/evKpYsaK+++47JSQk6NFHH1XBggU1Z84ctWnTRjlz5tSPP/6ouLg4FS5cWC1atNDKlStVqVIlRUZGatiwYRo7dqyioqIUGRmp/fv3Z/Zh4gbasmWLbr75ZllrfULJV3Ykeu+3L126pLCwMGfkJe8cpd75SqWU6V8mT56sHDlyqGHDhjfoSPBPMXfuXFlr1blzZ3Xr1k1VqlTRwYMH9eGHH8rf31+tW7dWnz59dOTIEeczBw8eVLVq1VSwYEH98ssv+vbbb+Xv76/Ro0drxYoVevPNNzVjxgx9+eWXWrJkCdPnudi7774ra60qVKigWbNm6e2335afn586duzobNOgQQNn2o6PP/5YUsrwvt6RJnr27ElowsW816rY2FiVKFFCt9xyi8/IXd4a++qrr5ygzogRI5z1dEriStQUMho1hYx2+fJl9e3bV9Zavfjiizp//ryz7osvvlBgYKBKly7t833w888/19tvv62SJUsqX7582rRpk0+by5cvV3R0tPLmzatJkybdqEPBPwQ1BVw/BCdcxHvT5725875NlJCQoO7du8taq+LFi/9haGLPnj3KlSuXmjVrJillmHPvcObet3jhHpcuXdJrr70ma62eeOIJSdLixYsVFRWl+++/36m38uXLy8/PT08//bSOHTumy5cv69NPP3VGNPFasGCBihQposKFC2vRokU3/Hjwz/HLL7/ooYcekr+/vzO0+OnTpxUSEqIXX3zR2e7ixYtOh3d4eLhCQ0P1+eef69NPP9XQoUPVtm1bpyMqMjJSNWrU0NKlSzPrsJCJ3n33XZUoUUJ58uRReHi4atWq5Qwv16ZNGwUFBWnatGl65plnlC1bNnXs2FEHDhyQ9L8HGt43SySpSJEiuu+++3jY4WKTJk3S3XffrT179jh/j46OVvny5XXfffcpPDxcX3/9tSRp586d+uSTT7Rr1y5JUosWLeTxeBQfH6+ffvpJlSpVclL/OXPmZL5Il9qyZYvToZg6RHrlqDaJiYk6e/ascuTI4QQnrjwXnT9/Xh9//LHCw8N9QhOcs9zFO7rg/Pnz1bZtWydE2qVLFz333HMKDAzUwYMHNXPmTH355ZeSpB07dqhkyZKqXLmyJKlQoUKMVoJ0TZ06VdZaRUVFKSgoSF26dHHW7d69W08++aQzbUfLli21YsUKxcTEMNIE0mjfvr2stWratKkTWvY+n9q1a5dWr16t++67T9HR0frwww+dz3nvzVN3Sg4YMMAJNnPNcy9qChmNmkJGiIuL0zPPPONMPbx+/XpnXWJiop599llZa3XLLbdo+vTpPp+NjY3Vr7/+Kul/3w+99eUdGbxbt2436EjwT3Hs2DFqCrhOCE64TGJiojNCgPfkuHPnTt1xxx2Kjo52UmaffPJJuqGJJk2aOG1VrlxZRYoUUfXq1bV58+YbfzDIdBs3blRAQIBKliypnj17Kk+ePHrggQecZPVTTz2l7NmzO3NxP/XUU04nk5TyJSEpKcnpwPz4449lrdWECRMy5XiQ+RITEzVp0iRZa32m1Vi2bJnPyCaSdOHCBb388svKnj27rLUqUKCAsmXL5owckCdPHjVq1EiDBw/Wzp07derUqcw4JGSi1A8ipk6dqnLlyik4ONgJZ7Vt21aBgYFO+G/x4sVOEKdTp046ePCgJN+Oy0cffdT5UhIfH89UHS527Ngxn7+//fbbyp49uwIDA/90WEPvQ7adO3dKklasWKGuXbvq0Ucf1ZYtW67rPuOf7WrCE4mJiTp16pTCwsLUtm1bSb7nOkITSO93/fnnn8vPz08vvfSS4uLiNH78eAUHB2vTpk0qW7asT+h53bp1OnnypCTpvvvuU9GiRXX69Gmud0jD+8wgLCxMK1askJQyjVWjRo3k8Xj07bffOtc87/36gAEDnO+KhCbczXtOWbNmjXLmzKmwsDA1btxYx48f16hRo/TAAw84nYs//fST7rjjDt16662aO3eu04b3OcLZs2d1++23K1u2bOrUqZPOnDlz4w8ImY6aQkajppDRtm3bpv/85z/OS4jr16/X5cuX9eGHHyosLEyVK1d2pli4sqP7jzRr1kzWWo0cOdIZhRDusWPHDmoKuA4ITmRRf/ZwdOzYsWrdurXzd++QPlWrVtWhQ4c0a9YsWWv16quvSko/NHH33XerQoUKiouLc4Y6h7t4a+zXX39V/vz5Za1V2bJlnfVt2rRRYGCgM6xTmzZt5Ofnp06dOun33393tkt98fUGLBgKyh3+6Mbr+PHj+uWXX3yWDRs2TEFBQU5I68iRIxoyZIiio6N1++23K3v27Prvf/+rw4cPa8+ePTp16hRBCRe6sqa8w9d7TZ06VaVKlVLBggVVu3ZtZcuWTRMnTpSUMuqNtVYPPvignnzySVlr1bFjR5/wRHx8vLp27aoGDRpo+/btN+7A8I9yZZ0lJSXpyJEjatq0qRPeaty4sZYvX66DBw9qwYIFmjJlil5//XUdPXpUr7zyiqy1PkMiJiYm+oxqAve6mvDEqVOn5PF4fEZhkghNIH1r166VtVa5c+d2RqF46qmnVLhwYV2+fFm33XabWrZsqZMnT2rQoEF64okndObMGcXHx+v2229Xzpw5FRsbSx0hXZ999pkzbce7776rpk2byuPx6KuvvpIk7du3T6Ghoc6Ued4pXniGAK/du3crX758stYqOjpaTZo0ce6/vR2OUsq9esGCBVWzZk2fF3e8AZy4uDjFxMQoLCxMhw8fvrEHgX8UagoZjZrC/8fRo0f122+/OX/fv3+/unTpImutHn30UQ0aNEghISFq3LixkpOTNWvWLAUGBqpIkSKaNm2a87n07sWHDh3qdIofOnTohhwPMpf3uUDqAPKePXuoKSCDEZzIglLPbTx+/Hifi7OUclPnvbGbN2+ewsPDVaxYMfn7+6tcuXIKDAzU66+/LillHtL0QhO33Xabc5MI9/JeYDdu3Kibb75ZRYoU0aeffqr27dsrKCjIeYt70aJF8ng8Kl26tKy1atOmjU94Qvrf1DCFChXiwpxFpXdD9lep1eTkZB05ckR33nmnihYtqrNnz+rw4cMaNmyYcuXKpS5duui3335TSEiIBg8e/Ic/j3Rs1uf90pCQkOBzfboyPDFt2jSVLVtWHo9HPXr0kCR9/fXXzlQJ3s5Gb/gr9cgTXt43cQEppVOodu3aypkzp1avXq0BAwY4b+DmyZPHqS1vMLB///6y1mrbtm2Zvev4h/qj8IT3/n3+/Pmy1mro0KHOOkIT+DMvvfSSChYsqLp16+rgwYOqWbOmqlSpIkkqWbKkOnXqpMTERBUrVkwPPfSQpJSaKliwoLMd8EemTp0qj8fjjPr2zTffSJJ+//13FSxYUNZalStXTtZaPf7449q9e3fm7jBumNSjs/3ZCCNt2rRR9uzZdd999ykqKkpNmzZVXFycJN/p8rxDR/fv31/S/65x3j/37t2b5jkDshZqChmNmsL1dPDgQfXu3Vt169Z1RpyUpMOHDzujcllrVadOHZ/6mz17tgIDA1W8eHGf6V+ufLa5d+9ebd26VXv37r3+B4NM5/39X7p0STNnznSCyhI1BWQ0ghNZjPdkFx8frzFjxjhvPaZn3759ztA733//vd5//31Za1WkSBHt3r1bhw4dUq5cuXw+7w1NkIx1F29dpb6YXnnz/+uvv+qmm25SYGCgsmXLpo8//liSNHfuXOeiPW3aNL399tvOtB2pvyxcvHhR27dv18aNG2/UYeEGSt1x06NHD33++efO3/8q1LBo0SIFBASob9++klLSr7ly5VLnzp2dbXLmzKl27dpdVXvIOlavXq1Zs2Y5f7906ZK+/PJLVa1a1SdFfWV4YvLkySpdurSKFCmiYcOGyVqrunXr6o477lD+/Pmd7dq2bStrrTp37qyzZ88ypDTSSEpK0nvvvadixYrpiy++kJQyDOtjjz0ma626d++uTz/9VJ988onefPNNbdy4UdWrV1e2bNl09OhRzlf4Q38UnpCkgQMHylqrGTNmSJLOnTtHaALpSl0DI0aMUKFChVSvXj01b95cmzdvVlJSkm666Sa9/PLLunz5svLnz69OnTpJkl599VVZa9WkSRNdvnyZ8xX+1AcffCBrrebPny8p5aGr9xz2yiuvSPrfHPENGjTgnsoFzp07p2nTpqlXr17OCCN/9Hvv3bu3IiMjtWDBAnXp0kU5cuRQkyZNdPToUUm+nZKPP/64IiIitH///ut/EPhHoaaQ0agpXE+xsbEaMmSIAgMDNWTIkDTrDx8+7IwC/uijjzpTqnv17t1bHo9HpUqV0nvvvXejdhv/UKlDEx988IFy586tZs2a+ZyzqCkg4xCcyEJShyZef/113XTTTXrggQd04cKFNNsmJCRo9uzZPp2PSUlJeuutt5xpO3LlyqXmzZs7nyE04S6xsbE6f/688/eEhAQtWLDAJ5V4ZXhiw4YNuvnmmxUREaHZs2c7b0SWKFFCHo/HmYJj+PDh8ng8ateunfPGEQ/Psq7UD+2rVKkia62qV6+uH374wVn+Zw/jFy5cqBEjRmjOnDkaOnSowsPD9fzzzzvrDxw4oBw5cqhr167X5wDwj3T48GEVKFBAd9xxhzPlwYIFC3T77bfLWqugoCCnI1tKG56YP3++oqOj5efnpzp16mjjxo1OJ9HatWud7W6++WZVqVLF53wIpHb06FGtW7fOp77GjRsnj8eT5gHJxYsXVapUKd133303ejfxL5ReeOL8+fN6+umn1aJFC/3444+Kj48nNIE/lfoea8iQISpXrpyeeuopxcbGateuXfL399ebb76p48ePKzw8XMOGDdOBAwfUqVMnBQUF6fvvv8+8nce/yoEDBySlDBfsPXel7oxKSkrSk08+qfXr12fmbuIGuHDhgqZOnapChQrJWqsXXnjB6VRM/b3fe63yvlzhfUO7Q4cOypkzp0+npPdzr732mqy1mjp1qk8byNqoKWQ0agrXU2xsrIYNG6bcuXNrxIgRzvL4+Pg027300kuy1qpRo0bOy4TffPONbrnlFme6s7vvvpsXDV3M+33u8uXLmjx5svLly6fSpUs7fX6pzzHUFJAxCE5kAak7g7yhiZtvvlkPPPCAc0FOPeea14ULFzRnzhwdO3bMaUeS3njjDVlrlStXLieZVrlyZUITLrJmzRpVqlRJS5YskZRy8z937lxVrlxZOXPm1JgxY5xtvbXn/YKwbt06xcTEKDIyUtZa1a5dW7NmzVJkZKTP6CXeOd69I1Mga7oyNBEVFaX//Oc/Cg0N1X333adly5Y56/8sPHHixAn17NlTISEhPiNNSNKKFSvk5+fnTA0Dd7h48aLGjRunoKAgPfzwwxo3bpxKlCihMmXKaOTIkcqePbvCw8M1e/Zs5zOpH4DMmzfPeZP2p59+kiS9+eabstY6U1z16NFD1lrde++9OnXq1I09QPwrpQ6xFi1aVA888IDP+nHjxvmMBsYb3PgrqcMTkydPluT7RtvkyZMVFhZGaAJ/KvW5pnfv3oqKilLfvn21ZcsWjRs3TsuXL9eOHTt87s0PHTqk9evXE27GVfHWyf79+31CE6mfU8AdvJ2RISEhatiwoapXr67g4GA9//zzunTpkqS0L03s2bNHPXr00OrVq51lzzzzTJpOSSnlu593VC+4AzWFjEZNIaOdO3fO6Xs5dOiQhg4dqqioKJ/QxPLlyzVmzJg0fStHjhxxplh44oknNHbsWJUtW1Z58uTRgQMHtHr1ao0dO5ZnBy7lvZe+fPmypkyZorx586pcuXJOaMJbd6nrg5oC/v8ITvyL/frrr9q1a5fz99ShierVq/9paMJ7I5iexMREjR07VtZa1atXTxUqVFCFChV8bgKRdSUlJemDDz5QRESE7rjjDm3btk0LFixQiRIllC9fPuXKlUsRERF666230v2slJKu9vPzU+PGjbVmzRpdunRJxYoVU506dZxtO3bsKGutBg8ezIU6i7oyNJErVy598MEHklJGkQgJCdE999zjBHSk/52vvJ/1/v23335T3bp1nek4pJSO84SEBPXq1Usej4c3Il3q/fffl7+/v0JDQ1WwYEFnntHx48crW7ZsioiI0Ny5cyVJ33//vX7//XclJCSoffv2qlu3rpYvX+605R3S7tChQ0pOTtaQIUNUsmRJbd68OVOODf9O3odsnTp1UkBAgFauXOmsW7RokTp37uwTGgP+SurwhLdT+9y5c/roo48UGhpKaAJXJfX9dsuWLRUWFqY5c+Y4y2bPni1rrb799tvM2D1kAXv27FHevHnThCYI37iHtzMyNDRUTzzxhCTpxx9/VPbs2ZUjR4503+i+8rqV+vlVem90//rrr87olcj6qClkNGoKGW3x4sUqW7asevbsqUWLFmns2LFpQhMrVqzQAw88oMKFC2vv3r1p2jh06JC6dOnijJ4aFRWlffv2SfKtP77rucP+/ft1+PBhn5Em0gtNXNmfkvrcRE0B/z8EJ/6lli5dKmut2rZtKyll2N7XX39d0dHRqlOnTprQhPckeLUjRiQlJenVV19VQEAAD9Bc6MyZM5oxY4by5cunYsWKqVChQipRooROnDihX3/9Vblz51ZkZKTefvtt5zOxsbGSpF9++UXBwcGqU6eO0yF58eJFFS5cWLVr15aUMpx+ZGSkgoKCnIs2spb0QhMTJ070mTpo3rx5CgsLU8WKFfX11187X0q3bdumxo0bOzXllbrz2hv+unjxosqUKaN77733eh4O/mFSP4ifP3++8ubNKz8/P9WrV0979uxxtpswYYKCg4MVGRmpXr16qUSJEs5bHzt37tT27dt92u3UqZPCw8N9ao+RlvB3zZgxQ9Zan1FPJDqQ8PekDk9MnDhRX3/9NdNz4JqlrpF77rlHpUqV0okTJyRJrVq1Unh4uI4fP55Zu4d/scuXL+uZZ56RtVYvvvgioQkXSt0Z6R1Za926dSpfvrxiYmJ09913K3v27GratKkzfUtqqR/+p64b7xvd3u+HCxcuVHh4uFq0aKGEhARewsjCqClkNGoKGS0xMVHjxo1zpnwJCAhQRESEhg8f7myzevVqNWrUSPny5dOqVask/a+WUtfRvn371K1bN1n7f+zddXQVV/c38O+5MeJCDAJEcKdYIYTgbqW4Fae4FLcUhwQKBRqkUIpTvLhrixMkAYpLghMgIW73+/6R904TpL+2DySU7M9azyqZOzO5Z2U/Z2bO2bOPYr169WS8PIvavXs3vby8OGPGDJKp49+GpImiRYtqywgbYujq1atcu3atdnza5AmJKSH+PUmc+I86ceIEc+XKxVKlSvHevXsMDAxkzpw52ahRI5Kpb3MbOkpDpuzZs2dpamrKpUuX/q3fERcXx4kTJ9LW1lbets1CDDdtr1694vDhw2liYsJs2bJpN3ck+fvvv9PR0ZFOTk6cM2cO16xZw169evHatWuMiori5MmTefLkSW3/e/fu0dHRkZ06dSJJRkVF8fLly+ne9Bafjr+TNEGmLgmTL18+mpmZaX3MlStXtGVeWrdurVUPSCvtg8Xo0aOplOLs2bNJStn7rCQxMZG7du1igQIFWKBAAc6YMYPGxsZs164db968qe03b948mpuba2v4RUZGvvV8ERERLFSoEMuUKcOUlJS3VmsS4p84efIklVLs2bMn9Xq9xJT4n6VNnlBKsUmTJtpnkjQh/i7DvdLRo0dpY2PDjh07kiT379/P7du389WrV5n47cR/2d27d7lo0SJJmsiC3jYZGRwczGLFitHLy0t7scfOzo52dnbasnhPnjzhH3/88dZzpo2fbt260dzcnG3atOGTJ0/46NEjbclZ8WmSmBLvm8SU+FD0ej0PHz7Mzz//nKamppw+fbr22dmzZ9m8eXN6eHjw/Pnzbz0+7XPcrVu32KdPHyql2KxZM165cuVDf33xkVm+fDmVUixbtizv3r3L5cuX093dnYULF34j4eb69ets1aoVlVIcN26cdo60Y08SU0L8O5I48R8VFRWlrVVUsWJFuru7s1q1akxISOCkSZOolOLkyZO1/UNCQpg9e3bmz5+f69at+9u/JyYmRrLRsqDExEQeOHCA+fPnp6urK52cnFixYsU31uxzdnbWJrnLlSunTUi+Pnh/9uxZKqX43XffkZRBtE/Zu5bneD1p4vjx4/T19aWlpSV/++03kuSlS5dob29PT09P1q5dm7a2tmzTpo2WPPF6XB04cIA5cuRg3rx5+eDBgw/cMpGZXk+ISUlJ4Y4dO/jZZ5/Rw8NDqwoxa9YsGhkZsXv37nz27Bn1ej2PHj1KMzMz1q5dW6tU8rYJxjNnzlApxS5dunz4Boks4cGDB9qyZ0K8LyEhIVRK8auvvtK2SdKE+DeioqL4xRdf0MrKSpKZxXsnz3tZx/81GWl4Tps2bRpNTEx45MgRkqnXM3t7e3bs2PGdCVtp46hjx47aWt2SLP9pk5gS75vElPiQnj59Sn9/f+bJk4etW7fm/PnzGRYWxosXL7Jp06Z0d3fnuXPn0h1Tp04d9ujR463nu3HjBrt3786CBQtqCTwia2nZsiVtbW3p5+dHLy8v5s6dm6tXr9bmZfR6Pa9du8batWvTysqKzs7OtLGxob+/v3aO15MnJKaE+GckceI/yHBTdvnyZdra2lIpxdq1a2sTk5s3b2b58uWplGJgYCBv375NOzs7lixZkjt37szMry4+Qq/fzMfFxXHbtm0sXLgwixUrxmvXrvGXX36hvb09K1WqlG4CPCAggEop1qlT563raxkG8idPnkylFDds2JABLRIfA29vb9ra2nLp0qVvlDg0JE1ky5aNR48eJZn6QGpnZ0cPDw9u27aNJNmzZ0/a2dm9NXni5cuX/PHHH2lkZMQ1a9ZkYMtERkvbp2zfvl1b9sDQ/1SuXFkrKx4fH88pU6ZQp9Oxb9++XLlyJd3c3Ojt7a1VX3rXW/9r1qxhmTJlePjw4Td+rxD/xh9//MGvvvqKc+bMYUREhMSUeG/SLkkkSRPif3HkyBG2adOGly5dklgSQvxjhslIa2vrv5yMHDNmDI2Njblnzx6S5Llz52hra0tnZ2eamJikWwf+9fultJOSXbt2ZUhIyIdulshEElPifZOYEh/S48ePOWXKFDo6OnLChAlcvHgxlVLs0aMHW7RoQS8vrzcqlrRo0YJWVlb87rvvtBcQb926pY2FkqnPezdu3MjQtojMZ+hLZsyYoVWZLFGiBPfv38/SpUszX758vHDhAsPDw1m7dm3a29vz7NmzvHz5Mr28vP4yeUJiSoh/RhIn/mMMN2fR0dFctGgRvby8qJTijz/+SPLPwdMDBw7Q19eXSimam5uzdOnS3Lt37xvnMfw3ISFBm1QSWUfam/1r164xJSWFt27doo+PD5VSPHToEEkyPDycc+fOpaWlJWvUqMGkpCTu37+fOXPmTDch+bYBV71ez3r16jF37tzvLJEvPi2GBwVvb+83HhiPHz/OKlWq0NzcnL///jvJPyvieHl5cd26ddqNYnx8PHv06EF7e/u3Jk8kJyczODhYqyIgPj1p+6itW7fS3d2dRkZG3Lp1K2NiYtivXz8qpVizZk1tIjE2NpYjRoygUoq5c+emnZ0do6Oj//L3JCUlMT4+nklJSXItFEL8Z8hEt3gfJI6EEP/Gq1evuGbNGtrY2LBDhw4k/95k5KVLl2hhYcHBgwfzzJkz2tjDX1VGleXOsgaJKfG+SUyJD8mQNOHk5KQl1jx48IC2trbaGOetW7c4f/58Dh8+nCTZqlUrWllZcenSpXzx4gXJ1AoTrq6ubNmyJR8+fJhp7RGZK+34508//URbW1u6ublp23r16kWlFMuUKUNvb29mz56du3fv1p7lgoODteSJgIAA7TipAifEvyOJE/8hho4wLi6Os2bNYo4cOVi4cGEqpVi9enVGRUUxMTFR22/p0qU0MTF5o/T46x3ms2fPOHv2bG7YsEEmILOQtBfkbdu20dvbm1OnTiVJDh48mEop5smThxcvXiSZmjwxY8YMmpiYsFSpUixVqhRLliyZ7nx6vZ7JyclaDOr1eiYmJnLz5s08efLkG8s1iE/T/fv3OW3aNBoZGbFbt268ffs2SfLEiRP09fWlubm5VhL6/v37tLW1pbGxMVeuXKmdI22sNGjQgNbW1mzRogUfP36csY0RmSZtH7Vp0yYWKVKEpUuXplKKFhYW3Lt3L5OSkrT+qmbNmnz48CH1ej1XrFhBJycnLUO7TJkybN26NdeuXcvDhw/zwoULvHDhAg8fPsxhw4Zpy8UIIYQQQggh/m87duygo6MjPTw8qNfrefv27b81Gens7ExbW1ttknHz5s3Mnj07ra2ttfL4ImuSmBLvm8SU+FDSJk0YxtLJ1GWtS5UqRRsbGx47doyJiYnMli0blVIsWbIkraysuHLlSu3FsHv37tHV1ZUlSpRI98KryFoM45+JiYlctmwZ8+XLp41nXrp0SduvV69e1Ol0NDEx4fz587VjDX3VxYsX6eHhQSsrK86bNy/jGyLEJ0QSJ/4jDB1obGwsZ86cSTc3N9aoUYNkahkwJycnhoeHa/tfuXKF9vb2zJ8/v5ZckTbbLG25+++++45KKfbv3z8DWyQyU9oJyc2bN7NgwYI0NTWltbW1Vr3EsLyGm5sbg4ODSaZmzrZu3VpLyPH29uauXbu05IrXz79///4MapH42MTHxzMgIIA6nY5dunTh+vXrWbVqVWbLlk2bpE5JSeHVq1fZtGlTmpmZ8fvvv2dKSkq6Nx/PnDnDSpUqaTeMo0ePljcjs5jNmzezaNGibNKkCS9dusSVK1fS1NSUdnZ2WvLEkCFDtOSJJUuW0M3NjXnz5mX+/PlZv359tm7dOt2Dh1KKlpaWNDU1pVKK06dPz+xmCiGEEEII8Z/x6tUrFilShKampmzfvj2LFi3KfPnyMTQ0lCTp5+dHExMT7t69myQZFBRES0tLOjo6UinFjRs3aueaM2cOlVKsUKGCNvYgsh6JKfG+SUyJD+HJkyccP348XVxc0iVNnD59mm3atKGFhQWVUjx//jzJ1GQce3t76nQ6Nm/eXKtyeuvWLTo7O/Pzzz/XKvKSsmxsVpM2aWLlypXMkSMHS5UqxR9++IFKKW054Xv37rFBgwY0NzenTqejj4+PNh+TkpLCuLg4kqmVnpVSWpUdIcS/I4kT/yHx8fH89ttv6eTkxOrVq2ulxw0TRps2bSJJnjx5ktmzZ2epUqX4+++/88iRI6xYsSKVUuku6M+fP+esWbPo4uLCjh07ZkaTRCbbtGkTixYtyi+++ILz5s2jm5sbbWxs+PPPP5NMnzxx7tw5BgUFMWfOnDQ2NqZSSosrCwsLli1blg0bNmTbtm1Zt25dlitXjoULF+aTJ08yt5Ei0yQnJ3PatGk0MzOjiYkJraysePToUZKpa6sZbv5u377Ntm3b0tjYmDNmzNAq3xw5ckRLmtiwYQMXL17M69evZ1p7RMa7cOECCxUqxDx58nD79u3a9gULFtDMzIz29vbaIMeQIUNoZGREpRQrVapEMvXtEScnJz548IDPnz/nhg0bOHHiRNavX58NGjRgw4YN+cUXX8jAhxBCCCGEEH+ToYppREQEq1SpQqUU7e3teevWLZJ/rs39yy+/kEwtH21hYUE/Pz9tQH/y5Mnpzjl8+HAqpdi1a1dGRERkbINEppOYEu+bxJT4UC5dusRixYrRwsKCQUFBJFNf+qpTpw5z5cqlvcxjmKcxJFO4ubnR0tKSCxYs4LNnz+ji4sLy5cvzxIkT2rklaSJrSkhI4LJly+jq6sqiRYsyJiaGwcHB2ovQd+/eZY0aNejg4MBjx45x9OjR1Ol0rFSpEi9cuJDuXIZ+qkuXLlJZXoj/gSROfOTSXjBv3brFGjVqsHjx4loWGUmuW7eOSin6+/vz1atXdHZ2poeHB3fs2KHtc+jQIVaoUIFKKc6YMYPx8fGcNWsWnZ2d0yVNyJvcWcfu3buZN29eNmnShOfOnSOZupSCi4sLbW1tteQJw8OEk5MTs2fPzsqVKzMoKIjW1tYMCAjg1atX2bt3b5YuXZo2Njba29zGxsYsVKiQtmabyJpiY2M5fPhwWltba5UmQkJCaGlpyQoVKmglx+7du8f27dvT2NiYgYGB3Lt37xvrSMoDRNYTEhLCTp06USnFUaNG8fnz51ocBAQEUClFBwcH7tixgzt27NCWrjI8HBw7dkw79l0kroQQQgghhPhnDJOSkZGRrFq1Ko2MjDh69GiOGzdOGxOIiIjgnTt36OjoyBIlSjAuLo7Pnj2jh4cHK1euzOjoaO3N2+fPn9PLy4vW1ta8du1aZjZNZBKJKfG+SUyJDyE6OppLly6lo6Mjy5Qpwy1btnDgwIF0c3PTqky4ublx2rRp7NixIy0tLblx40YePHiQJUuWpIWFBa2srFixYkUGBQVpyyzI2FTWlJSUxHnz5tHZ2ZlFixZlVFQUydTKJjlz5mSTJk345Zdf0sHBId0LZaNGjXojeeLIkSPa0jCnTp3KlPYI8amQxImPjCFxwXBzl1ZSUhJPnDihTQglJSUxIiKCRYsWpVKKX331FV++fMmZM2dy8+bNbxx/+PBh7e3thg0b0tXVVZImsqiEhAT279+fJiYmLFu2rLZdr9czODiYTk5O9PLy4pIlSxgfH882bdpoZfAN8efr60tPT09GRkZqN3dPnz5lcHAwL168yDt37vDmzZuZ0j7x4b3thv5dN/mhoaG8e/cuydQsfgcHB9ra2tLKyoo1a9bUMv5DQ0PZoUMH6nQ65sqVizqdjr/++ivJ1P5JHiKyppCQEHbp0oVKKQ4cOJCJiYl88OAB8+XLRw8PD5qYmNDJyYkNGjRg165d0yUWvnjxgh4eHqxfv7627fVYkrgSQgghhBDin0v7RrdhrMnGxoZt27ali4sL+/fvr71c4e3trR1Xv359FihQgDExMdq2V69e0dPTk+7u7loZfZH1SEyJ901iSnwI8fHxXLt2LR0cHFijRg0uW7aMf/zxB8nUF8hKlChBpRSdnZ35888/8/nz5yTJbdu20cTEhMbGxuzVq5d2PkNyjsiaBgwYwAIFCmhxkpyczJcvXzJ//vxUSlGn03HPnj0kma6KRNrkCcOLiffv3+eRI0cyvhFCfGIkceIjknbypnXr1gwMDNQ6zNelTXIwTGoXKFCAjx8/Tve5Xq9Pt+/27dvp6elJpRS7d+/+1vOJrOHx48fam9y1atXSYuDhw4d0dXWlTqejs7Mzp06dSjc3N1asWDHdhOSkSZNoZmamlSUTWdONGzd48eLFv5x8NjyoXrp0iXZ2dvTy8uL69eu5e/du2tnZsV69eumSJ3r06KGVFTOQye2swfB3Tk5O1q5nJHn9+nX26tVLu3Z5enqybNmyvHXrFhcuXEhra2u6ublx9erVJFOvaUlJSUxJSaGvry+dnJz49OnTTGmTEEIIIYQQ/3Xveh4zPOu9ePGCn3/+Oc+fP88zZ85ob3MPGzaM48ePp6WlpXZ/36FDB1paWvL27dvaeZo2bUqlFHv06MHY2NgP3yCR6SSmxPsmMSUyUmJiIjdu3EhbW1v6+Pjw7NmzWvWISpUq0cXFhVOnTtXmdm7fvk1XV1cWK1aMnp6etLS05IwZMzKzCeIjYlj+x9BfpaSksEqVKjQzM6NSinXq1NH2TTs/M2rUKG3J4rNnz5L8cz5QxtKF+Pd0EB8NpRQAYNasWVi7di327NkDExOTt+6r0+mg1+sBALNnz0auXLlw48YNtGrVCnfu3IFOp0NycjKUUtDpUv/MkZGRCAsLw/Pnz9GzZ0/8+OOPAAC9Xq/tIz5dJLX/Jicnw8XFBdOnT0evXr2wf/9+NGzYEA8ePEDlypWRO3duLFq0CACwcuVKNGrUCDt27EC2bNkQGxsLAChevDgSExOxZ8+edOcXWceFCxfQs2dPDB48GPfv33/rPnq9HkZGRrh69SqqVKkCe3t7TJ06Fc2bN0edOnWwYsUKnDhxAj169EBISAhy586NESNG4KuvvsLPP/+M+fPnQ6/Xa/2j+HSRhFIKycnJ2LVrF1asWIHnz58DAPLnz4+hQ4eiV69eWLZsGV6+fIlp06bBy8sLPXr0QEBAgHZtW7t2LXQ6HYyNjaHT6WBjY4PExEQkJSVlcguFEEIIIYT470lJSYFSCrGxsYiPj0/3mZGREZKTk2Fvb49jx46hVKlS2rPh6NGj4e/vjxw5ckCv12tjBo6OjkhKSkJiYiIAYPfu3QgKCkKuXLkwevRomJubZ2wDRYaTmBLvm8SUyGgmJiZo3LgxlixZgitXrmDIkCG4fv06AODLL7/EqFGj0Lp1azg4OCA0NBQVKlRAnjx5sGHDBqxbtw558+bF0KFD4e/vn8ktERkl7dyMYV7PsM3W1hYkYWRkBADaXJ2JiQnat2+PvXv3om7dugCAbNmyISEhAQAwefJkjBgxAsePH0f//v1x5swZbT5QxtKF+Pdktvwjc/78eSxZsgRFihRBYGAgrK2ttY7UwNChGjpQQ5KEiYkJLl68iK+++gr37t2DsbExkpOTAQAvX77E4sWLMW7cODRt2hTz5s0DIEkTWUXaCcnjx48jKioKQOqDwIQJE9CvXz/s3r0bnp6esLe3x/Lly9GlSxds2bIFT58+xaZNm7Bq1SokJSXBwsICAODi4gIAePHiBQDIxTgLSJscExISgoCAABw6dAhVqlRB7ty533qMTqdDWFgYypcvDwCYOXMmWrZsCQBITk5Gw4YNsXHjRpw4cQIjR47EsWPH4OnpidGjR6Ndu3aoUqWK9FGfsJSUFABAUlISlFJISUnBvn37MGHCBAwbNgy//fabtq+npycGDhyI3r1749WrV9i5cyciIiIAAD179sSsWbMQExODDh06YOXKlQBSr3Hx8fFaHyiEEEIIIYT4+wyJ8JGRkahRowaaNm2KuLi4dPsYGxtr+wJAWFgYAKB06dIAgPv378PS0hKOjo4AgFevXsHOzg7Ozs4AgJo1a+LevXs4c+YM8uTJkyHtEplHYkq8bxJTIrMYGxujUaNGCAwMRFBQEIYOHYqoqCh888036Nq1Kzw8PHD37l2UK1cOnp6e+P7775E/f36ULVsW8+bNQ7FixdCwYcPMbob4gCIjI3H9+nWEhITg3r17ePXqFV6+fAmdTofIyEjtBdXXxy3j4+MRFRUFT09PTJw4EQMGDEiXPGFmZqYlT0yZMgXDhw/HiRMn0L9/f20cVAjx78ls1EfA0JHFx8cjICAAoaGhWL9+PXLlyoXk5GRt0vDZs2cAUieoDcekpKQgISEBJFGuXDm0adMGx48fR4cOHXD37l0YGxvj0aNHWLhwIQICAlC3bl0sXboUgCRNZBWGt/VTUlKwZ88e9OzZE927d9c+z549O/z8/NC7d29YWlpCKYVChQoBACpUqIAtW7ZAKYWAgAAEBAS8kRH5emKP+PQY/saGG7jg4GBMnz4dO3fuxPz58zFmzBgAeOONfkPi1vnz55E7d26kpKTAyclJ29fw4Gpqaoo8efJg586dGDlyJPbv34/8+fNrSWTi07Rt2zb069cPer0eJiYmSEpKwr59+9C/f38t+W/s2LG4c+eOdkz+/PnRt29fLVFi4sSJeP78OZKTk3Ht2jUtYXDOnDm4f/8+dDodYmNjYW1trSV9CSGEEEIIIf5vhjGjV69eYcCAATh16hRu3rypPee9zlAx9cqVK7C1tdWS58PCwmBjY6MdFxoaiuzZsyM5OTlddUHDyxni0yUxJd43iSmR2QyVJwICAnDgwAEMGDAAAGBpaYlLly7B29sbHh4e+P7771GxYkXtBdhKlSrh9OnTKFq0aCa3QHxIwcHBKF26NEqWLAkvLy8ULlwYoaGhePXqFSZOnIgePXogOjr6jZe9DJUnXr16BXd3d4waNQr9+/d/Z/LE1KlTMWzYMCxcuBDZsmWTl8eE+F9l6MIg4g1p1xoaNWoUzczM+Ouvv5Kkti4WSYaGhrJLly5s3rz5G+e4evUqjYyM2L59e5Jkv379aGJiQl9fX968eZNHjx6lUopt2rTRjklJSflQTRIfgeDgYF6/fl37OTk5mbt27WKxYsWolKJOp+PmzZtJ/hmDz549Y79+/aiUYq1atbRjk5KS+MUXX1ApRQsLCy5atIgkefLkSSql2L9//4xrmMgUmzdv5s6dO0mm9jft2rWjk5MTu3TpwtOnT/PUqVNMTEwkmRovv/76a7r1HlNSUrhr1y6WKlWKzs7OPHTokPbZkSNH6OPjQ6UUx44dS0tLS9avX58xMTEZ2kaRsV69esXixYtTKcXVq1drMVKgQAEWKFCAkZGRnDx5MpVS/OGHH9JdD0ny1q1b7N27N5VSHDBgADt37sxs2bJx+/bt/PXXX7ly5UqmpKTw7NmztLS0ZLFixbT1AoUQQgghhBBvZxgfMNx/R0ZGsmvXrlRKsXr16tq62u9aNzspKYkVK1akp6cnnz17xsjISJYqVYqdOnUiSYaFhdHBwYGDBg3KgNaIj4HElHjfJKbExyg8PJwjRoygUooTJkxgYmIi8+TJw8KFC/PYsWPafmnj8l0xKv77DHNvMTExnDx5Mv38/Ni4cWN27tyZGzdu5KhRo2hqasqOHTu+9fhbt27R2tqa7u7ufPbsGUny6dOn7NOnD5VS9PX11fZNTk7+4O0RIqsxzuzEjazOkP31ww8/YOrUqfj+++/RpEkT6PV67W3s+/fvY8GCBVi1ahV69OjxxjnCw8NhbGysvU07Z84c6HQ6BAYGokOHDpg2bRrWr1+PZs2aAZBKE5+63377DVWqVEGbNm2wbNkykNQyXo2NjbFhwwZ07twZa9asQcOGDWFsbAy9Xg9HR0d8++23UEph7ty5qFWrFnbu3IkhQ4Zgz549GDt2LM6cOaOt43fkyBEAQI4cOTKzueIDO378OL788kuYmpqiR48eePXqFbZu3YpevXrBxMQEPj4+CAgIQPny5ZGcnIylS5eif//+aNWqFX7++Wetv6lTpw6MjY0xfPhwtGzZEjt27EBKSgpGjRqF48ePY+PGjWjatClq1KgBNzc3qQ7wibO2tkZgYCDq1auHzZs3w9bWFgMHDoRer8ehQ4dgY2OD7t27Y+vWrTAxMUm3DqBSCl5eXhg8eDD0ej0CAwORkpKChQsXokGDBul+j4eHBwYOHIg6derA1tY2M5oqhBBCCCHER+/atWtwdnaGhYUFzMzMYGxsjIiICAwaNAjLli1DgwYNsGHDBpiZmQFIHctKSEjQfja4cuUK7t69i1q1asHR0RFXrlzBxYsX0blzZwDA3bt3ERUVhYIFC2Z4G0XGkpgS75vElPiYZc+eHd26dcOVK1cwZcoUREdHo2/fvihTpgwqVqwI4M3lGKQqwKfpyZMnuHDhAgoVKqRVizCIjIzEtGnTsHTpUrRu3fqdleHt7Ozg4+ODWrVqwcbGBnq9Hk5OTvDz88PLly+xZs0anD59GuXLl9eqUwgh3qPMzdvImgwZZ4aswrt379LJyYnW1tbcsWNHun3v379PPz8/Ojo6smfPntr2tJlkz58/565duxgaGpruWMPbuB4eHrx371663y0+XRERESxbtizd3d1569YtHjlyhPny5WO+fPkYFhZGkmzXrh1NTEx47tw57ThDbDx//pz9+/enUoo5c+ZktmzZtOoU4eHhWtz+8MMPdHd355kzZzK2gSJDHTt2jCNHjqSJiQmVUjQzM+P3339PkpwzZw6VUuzRowfj4uK4YMECOjk5MW/evOn6I0PM6PV67t+/n5999hltbGy0CigbN25Mt5/4dKWtHJGQkKD1Nfb29ixQoADv379PklrfFBsby6ioqHee7/Lly+zduzdNTEzYu3dvvnr16o195LonhBBCCCHEuz1//pzlypVjoUKF2LlzZ968eZMk2aVLF9rb27Ns2bJ89OgRr1y5wtWrV3PixIls0KCBVi01rYcPH/LSpUu8evUqk5OTOXDgQDo7O/Phw4ckyYYNGzJfvnx/eY8v/vskpsT7JjElPpR3jUX+2zHKI0eO0M7Ojt7e3vztt9/+5/OJ/5aYmBhOnz6dOXLk4Pjx40n+OS756NEjjho1inZ2duzQoYN2TNqqzQZ6vf6NMU5DDD18+JAXLlz4UE0QQlAqTmQ4ktDpdCCJoUOHokGDBihfvjwWLlyInj17YuLEibCysoKvry+ePn2K+fPnY/78+Xj58iWePXuGsLAw5MyZM10mmYODg7a2EQAkJyfD2NgYgYGBCA8Px/r165GUlAQAUmkiC7C0tESPHj3w9ddfo1u3bnj48CFI4vDhw8iVKxf0ej1GjBiB1q1bp6sWodPpkJKSAgcHB/j5+SEhIQHr1q1DgQIF8MUXXwBIzZ41aNOmDZo0aYJcuXJldBNFBuD/z4L29vaGs7Mzzpw5g0OHDmHOnDla5Zvu3btj1apVOHLkCBYtWoQpU6bA2toaJ0+ehKOjI1JSUmBkZASllHa+6tWrIyAgAJMnT8aRI0cwadIkfPnll+nWjBSfHsPf/7fffsPWrVsxcOBAuLu7o0WLFli3bh1SUlKwYcMGuLm5Yc+ePahXrx5GjhyJyZMnAwCeP3+OxYsXo169erCzs0OePHkAAEWKFEHv3r1x//597N69Gz179kTx4sXT/W657gkhhBBCCPFusbGxiI2NxbVr13Djxg3o9Xro9XqsXLkStWvXRvbs2VGpUiXcuXMn3XElS5ZEkyZNtJ9JIkeOHNo4w/379+Hk5IRatWohR44c2Lx5M06cOIHu3btLhcFPnMSUeN8kpsT7FhkZCaUUYmNjER8fD2tra8THx8PNzQ3An9Ug+FqViP+Lr68vxo8fj4EDB+L48ePw8fHR5mrEp8/CwgIlSpSAUgqzZ89Go0aN8Nlnn+Hp06eYO3cuFixYgCZNmmiVJsLCwnDy5ElUrlwZrq6u2nmUUrC2tk53bqUU9Hp9uj7sn8anEOLvUeT/r38tMpS/vz/GjBmDTp064YcffoCZmRn27NmDtm3bomDBghgyZAjOnTuHwMBAdOrUCQUKFMDgwYNRrVo1zJ07F15eXgDe3TkaJiwB4OHDh8iZM2eGtk9kvJCQEFhYWCBv3ryIjIxEs2bNcPDgQRQvXhy7d+9Gjhw5sHTpUpw8eRLTpk2DnZ0dgNQbxaioqDcSIB4/foxRo0Zh6dKl6NSpE5YsWZIJrRKZLTg4GP7+/ti+fbt24w9AK3f4zTff4PvvvweQuizC6dOn4ejo+NaHAkN/RRK7du3CpEmTEBQUhMOHD2tl68SniySqV6+OI0eOYM2aNWjevDmMjIwwbtw4TJ06VYuD06dP45tvvsHly5cxaNAg+Pn5ITAwEP369YOZmRlsbW3RtGlT5MuXD+3bt4eLiwv++OMPREZGokSJEjK4IYQQQgghxD/06NEjhISEYOXKlQgLC8OFCxdQvnx57NixA6NHj8ahQ4fg4uKCWrVqITExETY2NqhduzY8PDz+z3MbngPXrFmDLVu2YNy4cShUqNCHb5TIVBJT4n2TmBLv05o1azB16lS8ePECsbGxsLKyQlJSEipUqIDChQujdevWyJ07N+zt7bVj/q/lzw1x9ODBA9SpUwdPnz7F6dOn/1YMiv+ut83PzZkzBwMHDsSoUaPQuXNn/Pzzz5g/fz4aNWqkJU08evQIK1euxI8//ohFixahatWqkgghxMciA6tbiP/vypUrLFOmDCtUqMAHDx6k+2zv3r3Mnj077e3taWtry9GjR5MkX758yWnTptHc3JwNGjTgrVu3/s/fk3Y5D/FpMpRoCg0NZaFChVi9enVeuXKFJBkcHExra2t+/fXX2r6VKlWiUoojRozgy5cvSZLNmzdnnjx52Lt3b86ePZthYWF88uQJSfLBgwccOHAgV69ezfj4+IxvoMhwaUvHhYSEsEOHDnR0dOTs2bO17QkJCdq/Z8yYQaUUixQpwvDwcJLpl2N41/n1ej03btzI8uXL8+rVq++7GeIjtGrVKjo4OLBz586Mi4vTtt+4cYM5c+ZktWrVtPJ0wcHBrF+/Pq2srPjdd98xKSmJmzdv5tdff013d3caGxtTKcW+ffu+sfyVEEIIIYQQ4u8zPN9FRESwU6dOtLW1ZY0aNbR79j/++IMkefr0aQ4fPpzz58/Xjv0n9+ApKSlvLUctPj0SU+J9k5gS71vXrl1pYWFBLy8vFi9enGXLlqWzszNNTU215auLFy/OZcuWvbEsQtqxzXfFV9u2bamU4pw5c2QJ2U9Y2r//06dP033WunVr2trask2bNnRwcEi3PMeTJ084ceJEmpubUynF/v37Z9h3FkL83yRxIoOk7US7dOlCGxsbXrt2Ld0+honsffv2MUeOHHRycuL169e1z58+fcqAgIB/lDwhsoYxY8ZQKcWxY8cyOjqaJBkXF8cOHTrQ0tIy3ZpqTZo0YbZs2Ths2DA+fvyYY8eOZcGCBamUolKKzs7OLFq0qDaZHR8fn26iXGQNISEhbNeuHW1sbNinTx8GBARwxYoV3LdvH8nUxKyFCxcyR44czJs3L58/f07yr5MmDNI+YLy+Xpv4tBj+1uHh4WzZsiVNTU25cOHCdPskJiaySZMmtLKy4s6dO7UHyosXL7JBgwY0MzPjtGnTtP1v3brFgwcPsnXr1vzxxx8lYUIIIYQQQoh/yXDvHRERwS5dulApxTp16miTkUlJSdo+wcHBVEqxUaNGmfZ9xcdPYkq8bxJT4kN48uQJnz59yri4OG1c6d69exw9ejTNzMzo6upKBwcHKqWYPXt29uzZk8uWLeP9+/e1MVAyNaknOTmZhw8f5qFDhxgWFkaSfPz4MTdu3Ki9ZCY+bR06dGD9+vV548YNbdu+ffvo6OhIpRS/+uorbbshaUIpxXHjxrFRo0asWbPmG+eU8U4hMo8kTmSwhQsXUinFo0ePpts+b9489uvXT+tc165dS0tLSzZq1Ih3797V9nv27JkkT4h0zp8/T29vbxYvXvyNCiZbt26lUop+fn7pKpAYJiNHjBjBpKQk6vV6nj59mkOHDqWPjw/t7e35+++/Z3RTxEfi3LlzrFWrFq2trTlr1iyeP39eS6xRSnH69OmcN28eHR0d6eXlxWfPnpH8e0kTBnLzl7WMGTOGxsbGtLCwYLFixbh+/Xrq9XpGRkZy4cKFnDJlChs1asSgoCAmJCQwKiqKcXFxvHXrFhs0aEBLS0uOHTs23TkNyYZCCCGEEEKIf84w0RgZGclOnTrRzMyMjRs31l6ceP0FiidPnjBv3rzMnTs3Hz58+M5nuvj4eK1ipbxlm7VITIn3TWJKZIS0Y+abN2+mkZER+/Xrx927d3Ps2LHMnj27NiaaI0cOenh4sH79+qxevTqLFSvGsmXLUilFBwcHBgUFZWJLRGbp1KkTlVJcsWIFydS+aPjw4bS3t6dSisuWLSNJPnr0iBMmTKBSipMmTSJJ+vv708rKio8ePdL6o7QxaXjpUMbShcg4kjiRga5du0Z3d3cqpXjs2DFt+3fffcd8+fLRysqK7du3571790imJlOYmZmxW7du6c7zevJE2kw2kTWkvVD27t2bSina2dnx0KFD2kPD8+fPef78eTZp0oQVK1ZkfHw8ExMTteMaNWpEU1NTDhs2TJv4Tk5Opl6v5+bNm+XBIQs7fvw4lVKcMGECydSs/jlz5nDq1KksWbIk+/fvTy8vL3p6ev6rpAmRtSxfvpxKKc6cOZPnzp2jq6sr8+fPz9WrV3Pw4MFUSnHatGlaIuCBAwdYunRpDho0iGRqIk+DBg1oZWXF8ePHa+eVJTqEEEIIIYT4dwwD8q9evWLXrl1pYmLCQoUKcdWqVVy1atU7xwOGDBlCc3PzdNVRyT/vzcPDw7l48WJ+//33H7YB4qMjMSXeN4kpkVl8fX2ZP39+rbLE1atXuWPHDjZq1IglSpSgUopGRkbpXjJTStHGxoaHDh2ScaosxNCvPH36lCVKlGDNmjX5/Plz+vn50d7enl988QVdXV3ZsmVLRkVFaZUmpk6dqp1j1apVVEpp1UrS+u677zhs2DBZQkiIDCaJExls9uzZzJ49Oy0sLHj+/Hn+9NNPLFmyJNu1a8fu3bvTwcGBPXr04IMHD6jX6zl+/HjtDe+0wsPD6e/vz2zZsrFx48Y8d+6cXJSzoB9++IFKKY4ZM4a+vr50cHDgli1bGBERwZ49e9LU1JTLli3TKpzs3buX48aN08rZ1a9fn+bm5hw6dGi6MmMGkjyRdb3+gGkwYcIE5s6dm3ny5NHWbpOkCfEu4eHhHDp0KMuXL89Tp06RJC9fvkxnZ2fa29vTyMiIo0aNYmRkpHbM+fPn6eLiwvz58zMqKorkn8t2WFtbp0uekOueEEIIIYQQ/0zasvedO3emTqdj48aNuXfvXnp4eDBPnjwk/7zXTklJ0Y6ZM2fOW6uokuTNmzfZv39/uri4sHjx4rx//34GtUhkNokp8b5JTInMYIgjw0s+r8/H6PV6vnz5kjt37uTatWsZEBDAiRMnctasWVy6dCmXLFmSSd9cZLbExEQGBATQ0tKS9evXZ7Zs2di2bVuS5KhRo6iUYtu2bamU4uTJk0lSqwJ++PBhKqV4/vx5kqkVcR49esT27dtTKcWyZcvy8ePHmdU0IbIkSZz4QNKW0yHTT0AvWrSITk5ONDExYd68edmxY0ftRm3w4MF0cHBg7969tTWzGjduTFdXVx4+fDjdOZ88ecJJkyZRKcXGjRtrk+Eia/jtt9/o5ubGevXq8cGDBwwODtaSJ5o2bUpjY2MOHDiQN2/eJJlaCspQNmrt2rXaeRo2bEhzc3MOGzaML168ICkJE+JPhiolCQkJXLx4pq0ycAAAae5JREFUMbNnz05PT09JmhD/yOtrOvbt25dKKXp6enLFihUMDQ3l/v37ef36daakpGifz58/X+uPLly4wAYNGtDc3Jz+/v6Z0QwhhBBCCCE+CVFRUfziiy+olGLt2rUZGxvLS5cuaQP0JLUxqLTjWydPnqRSimvWrNG23blzh+vWrWPOnDmplGK+fPk4duxYeTsyi5GYEu+bxJTILMHBwbSysmLnzp21bf9krFxe8smaLl68SGtrayql2Lx5c237tm3btKok9evX55MnT9ItF3Tx4kWam5tzw4YNvHjxIgcOHEgPDw+tr3t9TlAI8eEZQ7x3jx49wuHDh1G+fHnkzZsXAKDT6aDX66HT6dCtWzcopTB9+nTcvHkTM2bMgJubGwBgxowZsLS0REBAAGJiYrBgwQL0798fTZo0webNm1GlShWkpKTAyMgIzs7O6NixI/R6PZo3b45s2bJlZrNFBvP09MQPP/yAHDlyIGfOnMiZMycmT56Mjh07YsuWLWjVqhUmTZoEnU4HAHB1dUW7du2wbNkyrFu3DrVr14adnR22bduGRo0aYfbs2VBKYcSIEbCzs8vcxolMQxJKKe1nExMTJCcn48cff8SMGTNga2uLoKAg2NnZITk5GcbGchkRb5c2lrJnzw4AePbsGb799lssWLAAAHD37l0sXLgQGzZswNatW2FhYYFmzZph/PjxWLNmDfbs2YOePXuCJEqWLInJkyfD19cX+/btw9dffw1bW9tMa58QQgghhBD/VQ8ePMC1a9dQtWpVbNmyBdmyZUN0dDSMjY2RO3dunDhxAjVq1ICvry+KFy+O5s2bw8HBAU+fPgWQeh9/+PBhHDx4EJs2bcIff/wBkmjXrh1GjhwJd3d3mJubZ3IrRUaSmBL/i9fHokhKTIlMY2NjA0tLS2zevBmjRo1Cvnz5tPH1vyNtLIusI1u2bKhQoQJMTU3x888/AwCSkpJQp04dlCtXDmfOnMGuXbuQM2dOODk5IVeuXHBxcUFYWBji4+Mxbtw4XL58GQBQtWpVTJw4EXXr1oWjo2NmNkuILElmvN6z+/fvIyAgAKtWrcKsWbO0xAkgffJE165doZTCuHHj0K5dOxw4cAAVKlQAABQtWhQJCQmws7PDs2fPUKNGDXTp0gU//vgjevfujQIFCmg3lLly5cLIkSNl8jKLIQk3Nzct4QYAXr16hV9//RV37twBAJw6dQrbtm3Dli1bEBYWhkqVKqFPnz7o1KkTNm3ahCdPnmgJEtu2bUOtWrUwf/58eHt7o3HjxpnRLJHBDP1ISkoKXrx4AScnJyil3nhgDQkJwZIlSxAaGopNmzZpcSP9jngbQ/y8/qAYHx+PH374AYsWLcLKlStx69YtfPvttzh27BiKFi2KNm3awN3dHSVLloSLiwtq1KiB9evXa8ldALBp0yZERUXBzs4OZmZmmdE8IYQQQggh/jMMY1CvK1iwIDZt2oS8efPCxMQEQGrSvF6vh7OzM7y8vFCzZk0EBwfj8OHDmD9/PkgiX758AIBRo0bB2NgYycnJsLe3x5AhQ+Dr64sGDRpkaPvEx0NiSvxbacegTpw4gYoVK0IpJTEl/meJiYnYv38/KleuDGtr6799nLu7O2rWrIktW7bg1atXH/Abiv+av4qpAgUKYO7cubCzs4OVlRVIan2Xp6cnzp49i0WLFuHVq1f4448/cPv2bYSFheH27dswNjZGjhw50KFDB5QsWRJ16tTJjOYJIf4/mfV6j8LCwjBr1ixs3LgRRkZGqF279hv76HQ6rWJEly5dYGxsjGHDhqFGjRoICgrC1atX0bp1a7Rt2xY9evRA7ty5AQD16tVDYGAgfvrpJ/j7+6e7qZTJy6zl9UltAAgPD8fw4cOxevVqdO/eHbt27cKdO3cwcOBAlCxZEhEREViyZAnq1q2LqlWrYunSpQgICMCCBQu0C3jRokVx4MABvHjxIjOaJTKYIY6Sk5Oxc+dOHDt2DLVr10aNGjXeiC8PDw+0atUKwcHBWLZsGXLnzo0yZcpk0jcXH7PQ0FC8fPkSJUuWfOOzbNmyIVeuXFi0aJEWT7t27YJer8e1a9dgZGSEhg0bomXLllBKoXXr1li/fj1Onz6NmjVrIjIyEvv374eXlxfGjx8vVZaEEEIIIYR4B0PCxLFjx7BixQp06tQJZcuWhampqbZPoUKFAECrJHjr1i3o9XoYGRnBxcUFq1evBgBs2LABDx8+RFhYGPbv3w8A6Nq1K+zs7FCvXj14enrC09NTO+/bxizEp4kkIiIiYG9vDyA1ecIwziAxJf6OtMldAQEBGDFiBLZv34769esDkH5K/HskMWLECHz//ffo2LEjlixZ8rf+5obYcHJyQkxMDM6cOYPSpUtLzIi/jClDX1awYEFtf6WUtr1ChQpYt24dLC0t0aVLF22flJQUtGzZEmFhYfjxxx/h4eGR7vdJzAmROWTG/T158OABZs2ahTVr1uDJkyfImzev9uDweidnZGSExMREmJqa4quvvkJCQgKGDh2KkiVLIikpCV27dsU333yDwoULa8fUqFEDhQoVwsmTJwHgH5WHEp+Gp0+fwtnZ+a0XzOjoaGzZsgUBAQFo0aIFIiMjsX37dpibm+PkyZNYs2YN7O3tUbFiRcTFxWHmzJnYt28fnj9/DldXVyxfvhxz5syBh4cHatSokQmtExnJ8MCZkpKC/fv3Y9SoUbhy5QrOnTsHExMT+Pr6ptvf3t4effr0gV6vx9ixY2FtbY1p06YhZ86cmdQC8TF6/PgxVq5cid9//x1r1qyBlZUVjIyMAPx5HezevTuSk5NhZGSEzz77DPny5cP58+exYcMGdO7cGSNHjoROp0OLFi1Qo0YNDBkyBO3atYO5uTnMzc3x66+/4tWrV+mqOQkhhBBCCCH+RFIbM/r2229x+PBhVKhQ4Z3J74aXcS5dugQAcHJyApBaqtzY2Bg9evTQztuuXTscOnQIQ4YMSTc5kJYM8mcNJHH27FksWbIE1apV0xLgSUpMib8lbdLE7NmzMXLkSPTu3VurGJGWxJT4J0iiT58+WLVqFYYOHYr58+fDxMQE8+fP18ap/upYpZT2QlBYWBgAiZms7v+KqXfN1Rm2GxIiHj9+nO7zW7duYefOnRg0aFC6pAlAYk6IzCSJE+/BgwcP8N1332Hx4sVo27YtHj16hBcvXsDMzExLkABSs1/PnDkDf39/mJqaap91794dsbGxmDBhAvR6PZo1a6YlTZAEkHqD6OnpiYMHD+LOnTvpsmTFp+/JkydYsWIFSpUqhZo1a6ZLxiEJDw8PXLp0Cfb29jAzM0Pnzp2xceNGNG3aFLdv30bHjh0xf/58xMfHw9zcHDVr1sTOnTsREREBV1dXNG7cGBMmTED16tW1Kifi03Ljxg2EhISgYcOGMDU1RVJSEg4cOID+/ftDKQV/f38MHz4chQoVQunSpWFlZZXueCsrK3Tu3BkPHjzAvHnzUKJECQwZMiSTWiM+Ns+ePcOyZcvw3XffQafTpUuaANLf7BsbG2t92OjRo1GsWDH88ccfOHLkCLy9vTFq1CgYGRmhefPmCAgI0I7T6/VwcnLSBkiEEEIIIYQQbzLce8+ZMwdnzpzB119/jWbNmr1zqTu9Xg8A8PX1xerVq1GuXDkAf05UGiY3lVK4e/cunJ2d3xjcF5+2hIQEmJmZabFAEufPn8fgwYPx+++/48iRI7Czs0Pt2rW1N2wBiSnxbq8nTQwaNAiDBw/GwIED0y1L/PoxElPi/0ISffv2xYoVKxAUFIQCBQogT548GDRoEKKiovDjjz/+5bIdhrh0dXUFAERFRWXI9xYfr/81pgBo84OGRBzDdXXPnj3Q6/WoWLHiB2+HEOIfoPif3L9/n4MGDaKtrS0HDBhAkqxevTqrVq1KktTr9STJVatWUSlFpRRnz56tHZ+YmKj9e/jw4VRKsUGDBnzy5Mkbv8vPz4/Zs2fn7du3P2CLxMfm0aNH9Pf3p5WVFadOnfq3j6tfvz4LFizI/fv3s1q1anRwcOCGDRtIkg8fPkwXe+LTFhERwXbt2tHFxYVLly5lYmIid+/ezfz58zN//vx8/PgxSXLbtm28ePHiX57r0qVLrF69OnU6HY8ePZoRX1985J49e8aJEydSKcUcOXLQ3NycDx48+FvHPn36lIULF2b16tVJksHBwXR1daWnpyfXrVunXUNTUlI+2PcXQgghhBDiU3Py5ElWqFCBJUqUYHh4OEkyOTmZ5J/31nFxcf/onL///jtNTEzYvXt36vV6uUfPIoKDg+nv76/9nJyczKCgIJYvX57u7u4cOHAglVIsU6YMDx8+/I/OLTGVdRn+1t9//z2VUhwyZEi6sXDDWMA/JTGVten1evbu3ZtWVlYMCQnRtl++fJnm5uasV6+etu3ly5eMiIh457kOHz5MpRTbtm37Qb+z+Li9r5g6dOgQlVIcPnx4unOXLFmSFStW/HANEEL8K7Lew//g/v37mDRpEhYsWIAOHTrg+++/B5C6bIIhk18phbVr16J9+/aoVq0anJycMGbMGPz0008AABMTEyQlJQEApk2bhrZt22Lnzp3YsGEDgD8z/wGgTZs2OHr0qFSbyELCw8OxePFiTJkyBTExMbh37x6A1PWv/i+VKlXCw4cPYWVlhfnz56No0aLo1q0bNmzYgOzZs8PExCRdfIlPl5mZGerWrYvo6GisWLECv/zyC/r37w8AOHz4MFxcXEASDRs2hJ2dHa5fv65Vu3ld0aJF0b17d5BEYGAgYmJiJI6ymJcvX2p90LNnz7BgwQL4+flh8uTJWLduHeLj43Hz5s10x6SNp7T/dnJyQv369XHq1ClcuXIFxYsXx/79+xEXF4exY8di8eLF6UoNCyGEEEIIId7OcJ8dHR2NH3/8EadOnYK3t7dWTdDIyAhJSUnQ6XR4+PAhevTogePHj//t8wYHByM5ORmNGjWCUkru0bOA2NhYjBs3DiNGjMDy5csBABcvXkSvXr3w4MEDrFu3DrNmzcLUqVNx7tw5fPvttzhx4oR2/LvGCiSmsq4TJ04gNjYWOp0OgYGBWqWJMWPG4NatW9i0aRPi4uLeWaL+XWNVElMCAPr27Yvly5fj5MmTKFasGEgiNDQUrVu3hq+vL3bu3AkAuHv3LqZPn4758+cjJiYm3TkMsWSIwcTExIxthPiovI+YAv6sZGJnZ6dtmzRpEoKDg9G1a1cA7+7fhBCZIBOSNT4JiYmJDAwMpJmZGTt37qxtj42NZZkyZfjll1+SJJcvX06lFD09PXnt2jWePXuWZmZmzJkzJ9etW6cdl5CQQJKMiopi6dKlWbJkSS0rVrJjsw7DGyBk+re4hwwZwpo1a2pvZf9V5rXhs3v37tHExITffvstSfLChQusUqWKVnkiJibmwzVEfHRevXrFAQMGUClFMzMzFipUiI8ePSKZvo/p06cP3d3deefOnTfOkTbuGjRowJw5c/Lp06cf/LuLj8fZs2fZpk0bhoWF8fnz55wwYQKVUvTz8yNJ7t+/n0opbtu2TTsmbb9mkDbmFixYQFNTU+7bt0/bfv78eRoZGbFIkSJ8+fLlh22UEEIIIYQQ/3Fp769HjBhBExMT+vj40NTUlAMHDkxXufTFixf08PBggQIF+Ntvv/2t89+6dYu5cuVioUKFeP/+/ff+/cXHK+1b1ydPnmT58uXp5ubGU6dOpdtv/PjxWhXdoKCg//O8ElNZz/Hjx6mU4meffcZWrVpp451RUVHcsmULlVK0sbHhnj17tHFyg6SkpP/z/BJTWVdiYiJ79epFGxsbXrhwQdt+7949VqpUie7u7to4+L179zhs2DAqpTh//vx3nnPNmjVUSrFVq1Yf/PuLj8/7jinDHOHcuXNJkkeOHGHBggWZN29erRK0EOLjIWmX/5KJiQkqVKiAmTNnYsmSJQCA5ORkpKSkIDIyEgULFsT27dvRsWNH2NvbIzw8HFevXkWZMmWwadMmPHnyBOPHj8fu3bsBpK5zlJKSAktLS3Tt2hXBwcGYPn06AEh2bBbxxx9/4LfffgMAPHnyRHuLe+zYsZg+fTpSUlLw7NkzAH9mvb5eeYKk9pmFhQWcnJxw8uRJAECJEiUwa9YsFC9eHC1atMDmzZslkzELMPyNra2tUaBAAdjb28PMzAyzZ8+Gq6trupgBUivphIaGvjU7Nu1apZ9//jkePXqkvXUisfTpS0xMxLZt2/DLL79g6dKlWLx4Mb799luMHTsW48ePBwCYm5tDKaX1VQkJCTAyMgIA9OnTB9988w2A1OtacnIyAODrr7/G0qVLUapUKW293FKlSmHnzp3YtGlTumxsIYQQQgghRHpMU6Ftzpw58Pf3x9SpU7Fnzx7069cPs2fPxqRJk/D48WMAQIUKFZAtWzbMnDnzb62pHRsbi5UrV+LBgwdo3Lgx3NzcPmh7xMeDJKpUqYKhQ4di/fr1aNOmDR4+fIiNGzeifPny6cYBRo8ejZEjR2Lnzp3w9/fH5cuX33leiamsyczMDFWrVsWFCxewbds2DB8+HN9++y327t2LZs2aYdCgQShXrhw6deqEtWvXam/6P3nyBLNnz9bGN99GYirr0uv1mD9/PhYsWIAzZ86gZMmSSElJQVhYGOrWrYvQ0FAUKFAAc+bMwZkzZ7Bo0SJMnz4d3333HXr27PnO87569QoAZEwqC/oQMXXlyhUYGRkhX758IImbN2/i8ePHCAgIgIuLSwa3UAjxf8qsjI3/qlevXr11u+GN2tjYWBYoUIB58+alUorNmzfn+vXrmT9/fubIkUPLeF27di2NjIzo4+PD33//Pd25QkND6ejoyHr16v3jNSfFf9PNmzdZqFAhfv7557x69SonTZqU7i1ukmzZsiW9vLy0GEybbb1u3TqGhYVpPxsqAyxcuJBdu3bVMiD1ej137txJb29v/vHHHxnRNJGJDHGQmJjIXbt2sWjRorSxsaFSiv7+/rx79662r+ENpQ4dOtDW1pa3b9/Wjn9bhZOzZ89SKcV+/fplQEvEx+Lo0aNUSrFo0aJUSnH06NHpPj927BiVUly5cmW67Ya3SbJly8ZZs2Zp219/i0QIIYQQQgjx98XHx2v/XrlyJbNly8YxY8Zo1QVJctSoUVRKsXHjxvT09GTRokV54MABJiYmknx3RUvD9jNnzrBQoUIsXLiw9lbkX1XBFJ+GtH/jQ4cO0c7Ojk5OTjxw4MA7j4mJieGgQYOolGLnzp15/fr1t+4nMZW1pKSkaH/fCRMm0MzMjH5+fnz58iU3bdpEIyMjBgYGkiR//vlnKqW0cYOnT5/S19eXSim2bt2aV65ceevvkJjKWuLi4rRxzNjYWH777bc0NTVl48aNSZIREREsVKgQq1WrRpJaNYBKlSpRKaXFW3JyMidMmPBGXxUREcEmTZpQKcVevXplYMtEZvnQMXX9+nWeOHGCsbGx2rYzZ868tUqvECLzSeLEP3Dnzh1+9dVXXLFihbbt9c7t3LlzNDU1pbGxMZVS7NSpE0ly5syZVEqxd+/eWgf5008/0cjIiI0aNWJwcDDJ1Js6vV7PmTNn8ssvv3xnoob49FSpUoU2NjYcOHDgG0kTJNmxY0fmzJmTL1++TBd3bdq0oVKK3377LcPDw0mmfzh4fWIyOTlZ4ioLMMRAcnIyd+3axfz58zNfvnw8fvw48+fPTwsLC37++eecPHkyb968qQ2adevWjTly5ODz58/fet67d+/yyZMnPHHiBH19fdmrVy/GxsbKA2kWkJKSwmfPntHLy4tKKU6fPv2NfU6cOEGlVLqlqJo3b04HBwdOmDCB1tbWNDMzS5c8IQ8JQgghhBBC/DOJiYlcv349mzZtSpK8dOkSc+bMSUdHR54/f55k6ssWhhcuDMuAGhkZcezYsdp5Xl8a1nBvbni+S0pKYr169aiU4oABA9IlaohPz/3793njxg3t57Tx0alTJ1pZWfHq1asMCwujv78/lyxZwh07dmgviUVFRTE6Opp9+/bVXrS4d+9eut+RnJwsMZWFpI0hw9j40KFD0yVNGErXnzlzhnZ2duzbty/J1GWFypYtyzJlymgJYF999RUvX76snVOv10tMZTH37t3jqFGjePLkSS2+nj9/zsDAQNra2rJcuXIsUqQIa9WqpR0TFRXFUqVKUafTpdterFgxKqXYokULPnnyRNseExPDY8eOccyYMbx48WLGNU5kioyIqbRkHFSIj58kTvwDZ8+eZf78+WliYsJly5Zp25OTk7VJxtWrV2tr+uXNm5eWlpY8fvw4k5KSWL58eebJk4d37tzRjps1axaNjIzYrVu3dBOViYmJjIqKytD2icxhuCB/++23VEpRKcWRI0e+sV/z5s3p7u7OyMjIdNvMzMzo5eVFW1tbTps2TUueeBuZ3M4a0g5yGZIm8ufPr2XdN2rUiEopFilShEop2tvbs0KFCpwwYQJLlChBT09P3rx5k8+fP+ezZ88YEhLCwMBAduvWjV5eXtpDanx8vJZwIbKOevXq0c3NjUlJSTx9+jQDAgI4ZswYtmzZkuXKlaNSSsus/vLLL2ltba1dMy9evEhLS8s3Kk+8PmArhBBCCCGEeLeXL1+yfv36VEpx06ZNTEhI4ODBg+ng4MCqVaume1kiOjqaRYoUobu7Oz09PWllZZXuXpz8s6LlkiVL+MUXX3Dp0qXaZ/v27eOyZctkDe5P3KNHj9i0aVO2a9eOISEhb3z++++/UynFvn378vLly3R2dqZSijqdjvPnz+eiRYuYN29enjx5kg8ePGCXLl2o0+k4YsSIN2JHYiprSPuc//3331MpxSFDhjAqKuqNpImTJ0/S1taW3bt3J5n6dnfJkiVZunRp7RxDhw7VkiderzwhMZV1nDp1ioULF2bhwoUZFBSkjUuGhIRwxowZLFOmDM3MzLT9r127piXeeHt709zcnKNHj6aXlxd9fHz4008/0cbGhl9++eVb40fG0j99GRVTEktC/HdI4sQ/tHnzZubLl4/29vbaRNDZs2fZvn17bt26ld988w3d3d158OBBnjt3jtmyZePQoUNJkps2bXqjtHlSUhK7du36xhu6IuvZs2cPlVIcPHiwti00NJRnz57loUOHWLZsWVaoUEH7rHnz5rSxseGKFSt48uRJent708rKitOmTXtntQCRdSQlJfHXX39lmTJlWKhQIT579kz7bPz48VRKcd68eTx+/DhbtWpFDw+PdNVyPDw8aGtrS0tLS+p0Oi2pRynF7du3Z2LLRGYx3OB37NiRNjY2DAkJoYuLixYXtra2VEoxR44cTE5OZqtWrWhtbc0VK1YwOjpaO8+ZM2doaWlJW1tb+vv7a9sleUIIIYQQQoi/7/Tp0zQzM2Pbtm1JppaWHjt2LO3t7VmpUiVtv/z587NQoUI8ffo0Dx06xJYtW1IpxZYtW3Lfvn3pznnt2jXa2NhopalF1hEaGqpViujYsSOvXr1KMjWhYtu2bUxKSmKpUqX42WefMTQ0lLt37+bGjRs5efJkHj16lBs2bKBSSns7++7du2zXrh2NjY05efJkRkZGyqRRFpL2b/160sTGjRvfmjTRo0cPkqlJE0WLFmWNGjW0cyQnJzMuLo4jRox4Z/KEyBri4uK4adMmuru7s2TJkrx16xb/+OMP1qlTh61bt+acOXNoZWXFOnXq8OHDh9qLioZ46969u/bSa2RkJFNSUrhmzRpaW1uzefPmb1TJEZ++Dx1ToaGhmdxCIcQ/JYkTf1PaG75t27ZpyROTJ09moUKFWKxYMd6/f5+PHj3SbtwiIiJYokQJurm58eHDh7x9+zbd3NxYtmxZxsTEkExdRmHIkCFUSvGbb77JlLaJj8PJkydpYmLCIUOGcP369Vr2vlJKm8xu2bIlSbJp06bahKShBN358+dZoUIFWlpactq0aXz69ClJyWbMqp4+fcratWtTKZWuFCtJrl+/nkoprlmzhmTqA+iLFy+4bds2Ll++nF26dGGDBg3o6+vLGjVqsFGjRuzevTvnz5/PzZs3Z0JrxMfA0JcYBipCQ0O5YcMGnjp1ijdu3OC1a9fYrl07LW4sLCy4YsUK7Xqn1+u1N9kMyTsODg5vrbAjhBBCCCGEeDdD0nH//v3TJbfHxsbSz8+PdnZ29Pb2Zt68eVmsWDHu2rVLu5+/e/cu58+fTxsbG9rZ2bFJkybcv38/7927x4SEBA4aNIhKKe7fvz/T2icyx82bN9m3b1/qdDp269aNJ0+e5ODBg7XKJobnuKNHj75x7ODBg2ltba0t20GSN27cYLVq1WhlZcW9e/dmZFPER2L27Nla0sSLFy+4Y8cOGhkZceHChSTfnTRRu3ZtkuRXX33FH3/8UTtfVFQUx4wZoyX4PHz4MOMbJTJdQkICd+7cSU9PTxYqVIjVqlVj3bp1uXHjRsbGxnLevHn08PCgo6MjlVKcP3++dmzx4sXZoEEDbdycTB2vMsRqvXr1ePPmzcxolshEElNCiLQkceIfSDsBvX37drq7u9PY2Jienp7vXB5hyJAhtLKy0pIpunTpQqUU//jjD6akpPCXX35h/vz5mStXLp4+fTpD2iE+Tg8fPqSjoyN79+7NnTt3snbt2uzSpQv9/f351Vdf0cPDg4sWLWKnTp1oaWmZbkKSTI3Pq1ev0sPDg3ny5OHEiRPlASKL2717t1YRYMWKFdr2HTt2UCnFiRMnknzzTX9DXxcTE8OkpKS3Jt9IQk7WY/ibGwZS0w6IkanJOjly5NAqlrzeRxn8/PPPNDExYevWramUorm5ebqHCyGEEEIIIcTfs3XrViqltMqn5J/JE25ubjQzM+OWLVveeuytW7f41VdfMWfOnFRK0cLCgp999hltbGxoa2vLI0eOZGRTxEfi1q1b/Oabb6jT6ViyZEkaGxuzfv36JMnly5ezSZMmPHjwIPV6fbqxhKpVq/Lzzz9/43x+fn5USmkT4yLrmDRpEpVSHD58OB89esQTJ07QzMyM3t7eJMkLFy68NWmiTp06JMmaNWtSKcW8efNy48aN2nljYmJYtGhRurq6akvJiqwj7Xjk4sWLtYqmAQEB2ucdOnTg1KlTmS9fvnTLvRQqVIg1a9ZkbGxsuvOEhoayYMGCzJ8/P93d3Xn37t2Ma5DIdBJTQojX6SD+leLFi4MkjI2N8eLFC+zcuVP7LCUlRfu3l5cXYmJicOXKFQCAm5sbAODu3bvQ6XQwMzODi4sLunfvjoIFC2ZsI8RHJSUlBSSRlJSEevXqYc+ePVi8eDGGDRuGLl264NGjRxgwYAC2b9+OBQsW4Msvv4SFhQUAgCSUUjh16hTu37+PxMRE+Pn5Yf78+eniUWQdJFGnTh0sW7YMlStXhqOjoxYL9vb2AIBt27bhyZMn0Ol00Ov1AIDExETcvHkTAGBhYQEjIyMopUAy3fmVUhnYGvExMPzNw8PDAQAmJiYAgOTkZADA2bNn8fjxYxQrVgwTJkxAkyZNtD7KYMWKFejWrRu+/vprrFmzBhcvXkRQUBCcnJwysCVCCCGEEEJ8Gho1aoTmzZsjNDQUnTp1woEDB2Bubo4RI0agc+fOsLa2xvTp0xEXFwcg9d49PDwcp06dgpeXFxYsWIATJ04gICAArVq1grGxMXx8fDBw4EAUKVIkk1snMpLhmd/Lywvt2rVD6dKlERISgpw5cyIwMBAA0KFDB6xfvx7VqlWDUgo6XeqwckJCAm7evIk8efJAr9dr54qNjcXRo0dhYWGBqlWrZkq7ROZ49eoVQkND0a1bN/Tp0weurq6wsbGBh4cHQkJC0K5dO1SrVg0tW7bEwoULERkZiUqVKiFXrlzYvXs3fH19ERsbi3Xr1sHKygpt2rTB7t27AQDTpk3DlStXYGVlBQcHh0xuqcgst27dwpYtW1CsWDGYmppi9erVuHHjBn788Uds374dzZo1Q6dOnfDw4UMMHToUgYGBuHbtGnLkyAFzc3NtjOvOnTuoUaMGPD09cf36ddy4cQPu7u6Z3DqRGSSmhBAGkjjxFwwTiYZJIaUUUlJScPLkSdSuXRtOTk744Ycf4OTkhP79+2PZsmUAACMjI4SHh+PYsWPImzcvAGgPqTY2NgCAhw8fAgC++OILrFu3DkOHDtU+E58mQzwZ/vs6nU4HnU6HqKgobVtKSgoSExOxevVqJCYmwtPTEzNnzkTTpk3TTUgqpbB8+XJ06dIFvXv3xqpVq1CnTh20bt0aRkZGH7Zh4qNkSHaoU6cONm3ahOrVq2uxYBjEOHPmDBo1aoTQ0FBtwOPq1ato27Yt1q9fr50n7X/Fp+/1JJm0IiIiYGRkhNy5cyMpKQkAYGxsDADYsGEDjI2N8fXXX6Ndu3awtrZOd64VK1agc+fO6NOnD8aNGweSKF68OAoXLvxhGySEEEIIIcQn4PX7dMPYgmFC+vHjx+jatSv27NkDc3NzjB49Gn369MHly5dRu3ZtREZGwtjYGKdPn0bFihWxdetWmJubI0+ePBgyZAiWLFmCw4cPY8eOHRg7diwcHR0zuokiA7weR4YXLAzP/E+fPsXatWtx/vx5eHh44P79+5g0aRIuXboEIDWBPm1yBJCaRP/gwQOULl0aOp0OSik8ffoULVq0wJEjR+Dt7Y3q1atnUAtFZjLEhY2NDaZMmYIpU6Ygd+7cIIkiRYpg69atKFKkCNasWYPOnTvjxx9/fGvSRExMDA4cOIDmzZvDwsIC+fLl0yYeq1SpgpYtW2LDhg1wdXXNzOaKTKCUwqNHjzBw4EBERERg4MCBWL16NV6+fImmTZti3759KFWqFPLnz4+uXbuiVq1a+PXXX/H8+XNMnDgRv/zyC7p16wYAePToEWrXrg0vLy/s2rULAGQcPQuSmBJCvM44s7/Ax0qv10On0yE6Ohrfffcd8uTJg86dO8PIyAjnzp2DtbU1Vq1ahQIFCsDV1RUDBw7EwIEDodfr0blzZ4SHh6Nfv35a4oSVlRWAPx9IsmfPrv2uHDlyZHwDRYZ68OAB1q5di2bNmr0zwzA4OBjh4eHpEiwME5IXLlxAnjx5MHLkSDRp0gSWlpbpjl2xYgW6dOmCPn36YMKECbC1tYWPjw9MTU0/bMPER80w8GHobwyVSR48eAAAyJcvH86dO4cvvvgCW7ZsQe7cuXH69GkEBQXh8ePHmfa9RcYyXO8MDEk3b0uWsbOzw/DhwzFx4kS4ublpx168eBG7du2CjY0NHj16hOjoaNjY2GjnSZs08e2338pbIUIIIYQQQvwNcXFxCA8PR+7cud+4Tzfcwzdv3hx+fn5ITk6GkZERvv76ayxatAi1atXCiBEjAABz585FgwYNsH//fuTPnx8AcO/ePRw6dAhPnjxBQkICmjZtqr3Qk/b5QHw60sbP1q1b4evrCzs7O+3zlJQU7Nq1C9OnT0f9+vXx008/Yfr06Zg1axaSkpIwevRoFChQADqdLl3ihKFqpYuLi7bNzMwMzs7OqF+/PmbNmpXuM/Fpeb2fSklJgZGRUbqxbyA1/goUKICFCxfit99+Q9++fREZGQkfHx/kzJkTu3fvRuXKlREfH4+jR48iW7Zs+Oqrr3Dz5k0cOHBAe+miRo0aqFSpErJly5YZzRWZKDIyEiRhZmYGb29veHl5oVWrVgCARYsWoW/fvti0aRP69u0LAHB1dcW4cePg6+uL3377DRMnTgRJ+Pn54dWrV7h06RIKFCiAHTt2AHhzfEx8+iSmhBBvlTErgvy3GNboi46O5tChQ6mUYpMmTZicnKzt8+jRo3TrFm3bto358uWjnZ0dV6xYwQEDBtDV1ZUDBw6kra0td+zYwYSEBDZo0IBKKe7duzfD2yUyR2hoKL/55hsqpTh58uR37vf48WOOHDmSwcHB6bYvXbqUSik2a9aMt2/f1rYb4m/58uU0MjJi//79+fz58w/TCPFJ+emnn6iU4pIlSzh+/HgaGRmxVKlSjIqK4nfffUelFI8ePZrZX1NkgLRr0g4dOpQbNmzQfk57jXvXsYZ95syZQ51Ox/z589PMzIzjxo3j06dPSZLLli2TPkoIIYQQQoh/KCoqiuvWreMXX3zBjRs3atvT3qcnJyczKSmJDRo0YPny5Tlp0iRmz56defLk0cad4uLi6OfnRzs7O1apUoVr166lq6srx40bR51OR6UUXVxceO7cuQxvo8g4aeNm6dKltLOzY7Nmzd547rt48SJnzJih/Xzjxg3269ePOp2OHTt25NWrV7VzTJkyhSQ5bdo0+vr6cuXKlSSpjZ/Gx8czOjr6g7ZLZK6/00+llXZs/cWLF/zss89YsWJFkmTt2rXp4eGhfd6uXTs6Ozvz4sWLH+jbi49RREQEQ0NDGRERkW57eHg4x48fz+7duzMuLi7d54Z4mzx5MpVS9PHxYVBQEBMTE0mmztsYGRlpfduECROYM2dOWlhYaOdIOz4mPi0SU0KIf0oSJ15j6NBiYmI4evRoWlhYsGLFioyLiyNJJiUlvXV/kty6dSvz5ctHBwcHmpqasm3btnz27Bn3799PMrXD/fzzz+nk5MTw8PAMapHITKGhoRw0aBBz5MhBe3t7Xrt27a37GS7GhotvcnKytq1Tp05UStHIyIijR4/WJiNJmZAU/4yh/+rZsyeVUjx58iQjIiI4cuRIKqVYvHhxFihQgC4uLrx3797/OXEu/tvSXr+8vb2plGL16tXTJc38nRi4fv063dzcWLlyZd64cYM1atSgqakpJ0+ezHnz5tHIyIj9+vWTPkoIIYQQQoi/yTAZWapUKSqlWLp0ae7cuVP7/PX79HHjxlEpxXv37nHZsmV0cnJ6a/KEq6srs2fPTqUUjx8/zlOnTnHPnj2cPHkynz17lqFtFBknbbysXLmS+fPnZ6FChXj//v03Pk/7s+G/d+7cSZc8ERoayi+//JIWFhZMTk5mfHz8G+eR8YRP3z/tp9L+/PjxY5YrV45ly5YlSYaEhFApxTp16vDixYvs0qWLJE1kQYmJiVy0aBFLlCjBn3/+Wdv+4sULzpw5k0opfv/99+le5Ek7ttWgQQPmz5+fXl5eLF68OIOCgqjX6xkTE8OOHTvSxsaGly9f5vPnzzlo0CBaWFjw7t276RJ6xKdFYkoI8W9k+cSJtA8DhknFmJgYjh07ljY2NqxSpco7kyZePwdJHjx4kNmzZ6eNjQ3PnDmTbr9nz55x1apVnDVrlmRcZwH379/noEGD6OLiQqUU3dzctInDtz1Mvu2h8rfffqORkRE7derEbt26UafTccSIEYyOjuaKFSskaUL8Kz4+PsyTJ482MBYREcExY8bQwcGBSinWr18/k7+h+NBeT5pwcnLigAEDaGVlxcqVK/P333/XPv+rAa/ExEROnTo1XUWdFy9esEaNGlrC19dff82EhIQP1xghhBBCCCE+IVFRUVy7di0///xzrl27lj/99BONjY1ZqVIl7tu3L92+MTExJMl9+/axbNmyPHLkCElywYIFdHZ2Tpc8ERsby9GjR9PExITW1tYyIZlFvJ40UbRoURYuXJj37t3jr7/+ynr16mnjl3/1dmza5ImKFSvSzMyMs2fPJpl+vFQSJrKGf9JPGcbVDV68eMFWrVrR3Nw83fYdO3awSJEiNDU1pbu7u/RRWVBycjJ37dpFKysr1qxZk2TquNP48eNpampKpRQDAgK0/dP2N0eOHKG9vT0XLlzIQ4cOMU+ePCxRooRWTWn37t1USnHmzJkkU8ftb926lYGtE5lBYkoI8W9k+cQJQwkeQ6f46tUrLWmiWrVq6W7u/urm3/BZaGgoCxQowNq1azM8PFweGLIoQ9KEjY0NBw8ezA4dOrB48eJMTExM90C5atUq9u/fX/s57UPq8+fP2apVKyqluHv3bt6+fZtdu3alTqdjvXr1aGpqygEDBvDly5cZ2TTxH7d9+3Y6OTmxUKFCjImJ0fqoly9fsmLFilRKccCAASRlwONT9XrShIODA3/66SeSqQOulpaWrFSpEg8fPqztZ+i3DMcaMqfv3bvHAgUKMH/+/Hz8+LG2//Pnz9m4cWMqpdivXz++ePHig7dLCCGEEEKI/7q0k5GBgYGMjIykp6cny5Qpo73RvXTpUpLkgwcP2KZNGwYFBZF8c9J74cKFbyRPREZG8vPPP2fRokV59uxZkqnPffLs92lK+3ddtWqVljRhmNjp06cPlVLpluZ4Xdq3Zu/du6dVsPT09OSFCxc+3JcXH61/00+dOHFCO/7q1av09fWlUoqTJk1Kd+7169ezXLlykjSRBSUkJDAmJobR0dHaktfTpk3jxIkTqZTi0KFDtarMixcv1o7T6/VMTEzkmjVrmD17dh4/fpxk6qS2u7s7ixcvrvVVefPmpY+Pj1YlR3zaJKaEEP9Wlk6c+OWXX1isWDG2aNGC/v7+DA8Pp5+fH21tbd9ImoiOjqafn98bVSQMDA+oixcvplKK8+fPf+fvlQfST9v9+/c5cOBA2trasm/fviTJL7/8kqVKlUq336pVq6iUolKK48eP17YbYmnfvn20srJK9/b/9evX+fXXX1On07FMmTLvXPpDiHeJjIzkkSNHeOnSJZKp8WaIuVOnTrFly5YMDg7OzK8oPqC3JU0sWbKEsbGx2vbdu3fT2tqaZcqU4fbt27WBsmvXrrFVq1ZagoShUlNgYCAPHTr0xkPCs2fPWKZMGTo7OzM0NDQDWieEEEIIIcR/V9rJyPnz5/PVq1fMnTs3mzVrRjI1EUIpxe+++45RUVF0c3Nj1apV+eTJE+0cer0+3T3/woULtWU79uzZQzL1BaK0y/OJT1Pascfly5ezePHiLFGiBK9fv65t37ZtG5VS2gs9r8ePQdrlZOfOnavFoch6/m0/9fqS1efPn+eXX35JpRRHjRqV7rPIyMgMa4/4ODx9+pSff/45586dSzJ1/MnNzY3GxsbU6XRactepU6dYsWJFZs+enbt27Up3jsePH/PGjRvaz4mJidy1axe9vLxYsmRJ3rt3j4sXL+b8+fMlxrIAiSkhxP8iSydOjBs3jjly5KCLiwtz5szJ3r1709bWljVr1mRUVJS2X3R0NIsUKUKlFLt27ZruISOtiIgIOjo60tXVlbdv3yYpSRJZTWhoKLt160YLCwv27t1b2163bl1t3T6SXLt2LZVSrFevHsuWLUulFKdOnap9npyczOXLl/Ozzz7j5s2b0/2Oq1evsnfv3lRKccKECX9ZSlGIfyoxMTGzv4L4QP5O0gRJBgUFMV++fDQzM+Ply5dJkleuXNHWQm7duvU71z9+/Zp3584deQtJCCGEEEKI/8O7JiObN29OvV7Pp0+fsmTJkmzfvj2TkpJYpEgR1qpVK91Afdp78deTJ7Jnz04XFxcteeJtx4hPR9q/64oVK1i+fHkqpbhjxw6SqUsn6PV63rhxgw4ODixVqhTj4+PTLbHYqFEjtm/fPt15nz59Sg8PDxYpUoRhYWFv/C7xaXsf/VTaKrwXL158a/KExFTW8ejRI+3fpUqVYrVq1Uim9jUHDx6kubk527Ztm+6YrVu30sPDgzVq1Eg3qf0umzZt0ioNkH+9JJH475OYEkK8DzpkYd9++y3CwsJw7NgxlC9fHr/99hssLS2xdetWWFlZAQBiYmLw+eef4+7duyhVqhSWLFkCf39/3L59+43zWVtbY/DgwZg8eTI8PDwAAEqpjGySyEQpKSkICgrCsmXL0KxZMwQGBqb7zNTUFACwcuVKtG7dGvb29hg7dixWrFiBEiVKYNSoUZg3bx4AwMjICB06dMDBgwfRsGFDAABJAEDBggXx9ddfo0OHDqhatSp0uiz9f2PxnpmYmGT2VxAfgF6v1/qKSpUq4erVq5g+fTpat24Nc3Nzbb8TJ05g0KBBePToEfbv348iRYrg8uXLqFSpEmxsbFCrVi3s2rUL/fv3R3h4uHZug9eveR4eHihZsmQGtFAIIYQQQoj/pujoaOzcuRMzZ85Ep06d0K5dOxQtWhSff/451q9fj+fPn8PHxwcFCxbEsmXLULp0aTg7O2PPnj2wsbHB3r17kZCQkO5eXKfTaffpPXr0wIQJExAbG4u6devi4MGD2n4yZvXpIan9XVevXo1p06bh5s2bAICuXbviwoULyJYtG5RS8PLyQt68eREVFYWEhARt3Kply5bYuXMnXF1dERkZCQCIi4vDiBEjcO/ePfj6+iJXrlwAJIayivfVTxkbG2vjmyVKlMC4cePQtGlTTJ06FaNHjwYgMZVV3L17F126dME333wDkujUqRNiY2MxatQolClTBkFBQahYsSI2btyI3bt3a8c1atQI33zzDQ4dOoRt27YBSD8u9brHjx/D1dUVrq6uACDj6J8wiSkhxHuTqWkbmciQ4RoZGclJkybRzs5OWzZh4sSJJFMzXIsUKUJbW1suWLCAJ06cYKtWraiUYrdu3bQ1Acm3Z5ZJtlnWc+fOnXQVIhITExkfH89y5cqxa9eu3Lp1K5VSzJEjB3U6Hf39/UmmLsvh5eVFKysrLlu27G/9rujo6A/RBCHEJ8zb25u2trZcunTpG0trHD9+nL6+vsyWLZtWujckJIR2dnb08PDgtm3bSJI9e/aknZ0d27Rpo1WekOudEEIIIYQQ/1xUVBTXrVvHChUqMDAwMN0b3GTq8ncFCxZky5YtmZKSwhIlSrBq1ara8e3ataObmxvv3LlD8s378rQ/T5kyhTY2Nrxy5cqHb5jIFGnf1F+5ciWLFi3KokWL8s6dO5wyZQqVUnRycmJISAhJMiYmhpUrV6aRkZFWKbBly5Y0MTHhhAkT+PTp03TnX7NmDfv378+LFy+SlOfArOJD91MXL15kkyZNqJTiwIEDM6xdInOFh4ezcuXK6Zaw7tixI6tUqcIaNWowJCSEixYt0uZrpk+fnm7J6rZt29LBweEvq35funSJ3bp1o6enJx88eJAxDROZRmJKCPG+ZMnECcMNWkxMDMeOHUtbW1vWqlWLv//+O3PkyEGlFIcNG8aiRYvS1taW33//vZZocfr0aTZv3vytyRMia4qIiGBycvIb2w3bkpKSWLZsWVpbW1MpxQYNGvDkyZP09fWlq6urdoH+9ddf6eTkRDc3N27atEk7z+sXaSlZJ4T4NxYvXkylFL29vbWBMoPjx4+zSpUqNDc35++//04yNWkie/bs9PLy4rp167Q+LT4+nj169KC9vX265Im39YNCCCGEEEKIt0tISOCmTZtYqFAhLly4kHFxcf/nZKSh5DRJNm7cmLa2trS2tv7Lyca09+nPnz//cA0SmeptSRNFihTRlhvW6/UcNWqUljxhSH7Ytm0bp0yZQjJ1gtvExITTp09PFytpJ7pfT8AXn7YP2U+ljdnz58/T29ubZmZmbyTsiE+H4W9uWDL2wYMHrFmzJq2srNioUSOWLVuWTZs25f79+0mmxl/Lli21ie6qVatyzpw5JMmzZ88yZ86cbNGiBRMSEqjX699YfjgpKYn379/n/fv3M7CVIiNJTAkhPoQslzjxetKEjY0Nq1WrxpiYGJLk1atX6eTkRKUUs2XLxrlz575xjrNnz7JFixZUSrFPnz7y0JCF3b9/nx07dmRgYOA797lx4wadnZ1pYmJCnU7HRo0akUxdP8vY2Jj16tXTLvLr1q2jmZkZixQpol3QhRDifbh//z6nTZtGIyMjduvWTcugPnHiBH19fWlubs7jx49r+9ra2tLY2JgrV67UzmF4ECHJBg0a0Nrami1atODjx48ztjFCCCGEEEL8x0VFRXHMmDFUSrFdu3bMkycPW7ZsSb1er01GNm/enHq9nsWLF2fNmjW1l3oaNWpELy8vBgcHc8KECfTw8OD58+dJ/llhNS2pDJB1/PzzzyxYsCALFy6sveyV9jlu5MiRVErR0dGRQUFB2vZu3bpRp9Nx+vTpfPHiRYZ/b/Fx+tD9VNrkiXPnzvHu3bsZ3kaRsWJiYtiyZUu2b9+eq1evZlxcHEuUKEFjY2Pmy5ePmzZt4ty5c1mvXj3WqVOHTZo0oaWlJceOHctixYrRysqKVapU4YIFC1iiRAl6e3tr8zoGZ8+eZVRUVCa1UGQ0iSkhxPtmnNlLhWQkwxrvMTExmDp1KubOnYsyZcpg586dyJYtGwCgYMGCsLa2xosXL5CQkIAHDx5oxyclJcHExARlypTBqFGj8OzZM8ybNw9169ZFw4YNM6tZIhNFRETg8ePH6Nu3L5RS6NWrF4DUNSWfPXsGZ2dnhISE4NmzZ6hbty6SkpKwfft2rFu3Do0aNUL9+vWxfft2bNu2DY0bN0aLFi0QHx+Pjh07YuLEiXB0dETJkiUzuZVCiE+Bm5sbBg4cCJ1OhxEjRkCv16NevXoIDAzE6dOnsW/fPlSsWBF6vR7R0dGoXr06du7cifDwcG1tP3NzcwDA2bNnERERgejoaGzYsAEFChTAhAkTZF0/IYQQQggh/iYrKyt06tQJUVFRWLlyJUxNTbF27VpERkaiUqVKKFmyJNatW4fKlSsjKSkJ+/btAwA0b94c+/fvR1BQEAoXLoy4uDgEBAQgKCgIpUqVgrFx6lDfli1bULt2bZibm8t9ehZx6NAhdO3aFfny5cPevXuRK1cuAH8+xwHAlClToNPpMGXKFNStWxenT5+Gn58f1qxZA39/f3Tt2hX29vaZ1QTxkfnQ/dTWrVtRs2ZNWFpa4rPPPsvMpooMcvLkSbx8+RJhYWGwtLTEo0ePEBcXh5SUFERHR+PXX3/F06dPERMTg+zZs6Nt27bYtm0bKlWqhD59+mDz5s2YN28eRo0ahZcvXwIAWrVqBVtbW8TExODVq1e4desWFi9ejJo1a2Zya0VGkJgSQrx3mZ25kVEMGazR0dEcNmwYzczMWKVKFcbFxWn7REVFsUiRIrSzs+OwYcPo7OxMpRRHjx6t7ZOQkKD9u02bNlRKaeu+i6zpyJEjrF27NpVSnD9/PknyzJkz9Pb25qpVq7hgwQJaW1tzy5YtDA4OpqmpKdu0aUMyNZs6W7ZsHDBgQLpz+vn50cTEhPPmzcvo5gghPnHJycmcNm0azczMaGJiQisrKx49epQkeffuXR4+fJgkefv2bbZt25bGxsacMWOGdv07cuQIK1WqRKUUN2zYwMWLF2vlX4UQQgghhBD/zN27d/nNN99QKcXWrVuzUKFCWtn70qVLUylFHx8f7tq1i4GBgTQ3N2eBAgXYoUMH3r59mykpKezatSu/+OILnjp1iuvXr2elSpWYK1cunjp1KrObJzJY3759ef36dd6/f5/du3dnv379OHr0aK5fv547d+5kbGwsU1JS6Ofnp1XbNTEx4fz587VlGIV4nfRT4n1LSEhgQEAAq1SpwiJFilApxdy5c7NgwYLs06cPydRqOdHR0Zw7dy4PHTpE8s85nrVr13L8+PFUStHc3FyrIJ47d27mzp1b219kHRJTQoj3JcskTpCpHWOnTp2olGKlSpXeSJooWrQobW1ttXWNQkJCtOSJMWPGpDvXsGHDqJRi9uzZee3atQxth/g4pC0nd/z4cS15YuzYsSxcuDBLlCjBK1eukGS6MvYtWrSgra0tr1+/zoiICH722We0tbXljRs3SKbG6cSJE6mUYvfu3TO2UUKILCE2NpbDhw+ntbU1f/vtN5Kp1zxLS0tWqFCBly5dIkneu3eP7du3p7GxMQMDA7l37176+PhQKcV169aRTN8XCiGEEEIIIf65sLAw9u/fn0opduzYkSkpKSxRogTr1avHY8eO0dPTk/Xr1+fs2bP54MEDLliwgBUrVqS1tTVLlSpFd3d3mpub08zMjJ6enqxcuTKXLl3Kp0+fZnbTRAZ5fTmWgwcP0sjISFvH3fC/WbNmafsYJpasrKy058K3LfUiBCn9lPjfpR0/2rlzJx0cHFi9enXOmjWLhQoV4k8//cTKlSvT1dWVGzduJEn+9ttvbNy4MT08PPjgwYN057t8+TJz5MjBiRMn8s6dOwwJCWFERAQjIyMztF0i80hMCSE+hCyVOBEREcEKFSrQy8srXdJEdHQ0ixUrRgcHB86ePZvx8fHaZ2fPnqWLi8sbyRM//PADq1Wrxt27d2doG8THJe3F+cSJE/T29qZSinny5GFoaOhb95s6dSqVUvz9999JkiNGjKCpqamWYb1p0yYWLFiQzs7OPHbsWAa1RAjxKXhbEsO7EhtCQ0O19UODg4Pp4OBAW1tbWllZsWbNmtp6uKGhoezQoQN1Oh1z5cpFnU7HX3/9lWTq4JwkTgghhBBCCPG/CwkJ4YYNG5icnMzixYuzevXq2r12uXLlaGxszEqVKvHIkSPaMevWrePcuXNZoEAB5syZk0ePHuX9+/czqwniI5KSksKzZ8/y3LlzXLRoEUeOHMkKFSrwxIkTJMnWrVvT1NSUNWrUoFKKTk5OvHjxonasEG8j/ZR4X65cucI+ffrwzJkzvHPnDpVSPHnyJGNiYti+fXv+8MMPvHTpEuvVq0dbW1sqpbhq1SqSf/ZR165do1KKP/30E8nUCqsi65KYEkK8L8aZvVTIh0QSSintZ1tbW2zduhW2trYwNTVFSkoKkpOT4ePjg8uXL2Pq1Kno378/AECv10MphTJlymDHjh1o0KABJk+eDACYOHEi+vTpg1atWsHR0fGN3yM+bSkpKTAyMgIAKKWQnJwMY2Nj2NjY4PHjx7C2tkZYWBh27NiBnj17vnF8uXLlAACXLl1CpUqV4OrqiqSkJJw+fRrly5eHlZUVfH19kT17dhQtWjRD2yaE+G8zXItu3ryJ2NhYFC9e/J3Xp5w5c8LIyAiXL1+Gr68vHBwc4O/vD2tra7Ru3Rp9+/bFDz/8AC8vL0yePBnm5uZYtGgROnfujCZNmmi/T65/QgghhBBC/G+Sk5NRrFgxeHh4oHLlynj06BFOnjwJpRQaNGiAiIgIrFy5EsOHD4efnx8GDRoELy8vVKtWDY6OjjA1NcXSpUvh4+MDvV4PIHVcS6fTZXLLRGbR6XQoU6YMAOCzzz4DALx48QIODg5o3749Nm7ciO+++w5fffUVZs2ahQkTJqBmzZo4ePAgihUrJvEj3iD9lHifChcujICAAFhYWODo0aMAUvsoY2NjnDp1Crt370b+/PkRGhqKLVu2YMCAAdiyZQvatm2Lmzdvwt3dHU+ePAEAZM+eHQC08XqRNUlMCSHel08qceL1my2l1BtJDU5OTtq+RkZGSEhIgJ2dHQBg5cqV6NOnD6ysrEASOp0OJFGmTBls374dDRs2xOTJk5GcnIypU6fC0dEx3SS6+PQZ4iYqKgrLly9Hnz59YGxsjIsXL6JZs2awtrZGQEAAFi1ahN69eyMlJQV9+vQBAISHhyM4OBj58uUDkJrYAwDW1tYAgOjoaABArVq14OPjA51OBzMzs0xopRDiv+zChQsYMmQIlFJYsmQJcufO/cY+hr7s6tWrqFKlCuzt7TF16lQ0b94cALBixQp06NABPXr0wKxZs1C8eHGMGDECCQkJ+Pnnn1G2bFl8/fXXMsAhhBBCCCHEe2BsbIynT5+iVKlS0Ov1ePXqFRYvXow9e/bgypUr2LdvH/Lly4fly5djz549SExMxMuXL9GkSRNMmzYNx44dg5GREZRS2hiV3KsLg4SEBJiZmcHBwQFt2rTB+vXr4e/vjzZt2sDW1hbjxo1DUlISpk6diurVq2P37t0oXbq0vCgm0pF+SrxvFhYW0Ov1OH78OAAgf/78MDU1Rc2aNbFgwQIYGxtj69atKFeuHHx8fLBnzx40b94cV69exfDhwxEcHAxTU1N8/vnnmdwS8bGQmBJCvA+fzN1J2qSJYcOGYePGjQD+TJ54nU6ng16vh4WFBbZv346OHTvi8uXLqFixIp49ewYjIyOkpKRox5ctWxbbt2+Hk5MT/P398fXXXwOQrLOsxBBjMTExmDhxIvr164e+ffsCAK5fv45SpUph7dq1aNasGcaNG4datWqhX79++OGHH6CUQlRUFGrWrImxY8cCAMzNzQEAsbGxAABHR0ftd5mbm0vShBDib0l7jQsJCUFAQAAOHTqEKlWqvDVpAki9BoaFhaF8+fIAgJkzZ6Jly5YAUt8iadiwITZu3IgTJ05g5MiROHbsGDw9PTF69Gi0a9cOVapUkQEOIYQQQggh3pOUlBSMHj0axYsXx5kzZzBkyBAMHDgQBw8exLZt25AvXz50794dQUFB+PXXX3H9+nXcuXNHu59PTk6Gh4cHALx1DExkXXq9XhtfatWqFTZt2gR/f3906dIFjo6OSE5OBgBMnjwZw4cPR3h4OBo2bIjDhw9L0oRIR/op8SGQRJEiRdCyZUs4OTnht99+w7NnzwAAJUqUQLly5ZCQkICIiAjcunUL586dQ+7cueHm5oYbN24gV65csLGxyeRWiI+JxJQQ4n/1SVScSJs0UalSJZw4cQLVqlWDs7MzKleu/NbKE0DqxFFKSgosLS0RGBgInU6Hn3/+GdWrV8fBgwfh5OSkVZQwJE9s3boVlSpVwpIlSzB+/Hi4urpmRpNFBjPEWGxsLKZOnYoffvgB3t7eCAgIAAC0aNEC1apV08o4VahQAePGjQMA9O/fH0opPHz4EHZ2dihQoABy5MihxeOpU6cA/JlIIYQQf4ehXzL0JcHBwZgxYwZ27tyJ+fPno0ePHgCApKQkmJiYaMcZlhc6f/48cufOjYcPH2rVmNLua2pqijx58mDnzp149eoV/Pz8ULNmTSxZsiTd+YQQQgghhBD/GyMjI0yaNAlGRkZwdHTEyJEjYWFhgbFjx2L37t2YPXs2Nm7ciODgYNy7dw9ubm4IDQ3FzJkzER0djR49eiBnzpwAIJPdIh3DeGmzZs2wY8cOTJkyBV26dIG9vT2A1CoChrHPqVOnIikpCTNnzkS3bt0QEhIiY1VCI/2U+BCMjIxQr149NGrUCJcuXULfvn3x5MkT+Pj4IDQ0FBs2bMCwYcO0vujHH3+Et7c3jI2N8f3332txKISBxJQQ4n+l+B9P8Xw9aeLGjRto27YtfvrpJ3z22WeYOnUqKlWqBADvLDFneECIjY1Fv3798PPPP6No0aJvTZ5QSuHo0aNwcXFBwYIFM7StImMZ/t5pkyamTZuGWbNmoXTp0tizZw+yZcuG+Ph4ZMuWTTsubUweP34c48ePx8GDB7U1/xYvXowHDx6gRIkSIAlvb2/cvn0b+/btQ4kSJTKruUKI/5hff/0VZmZmqFevHq5du4aJEydi7969aNSoEXr27AmS+Oyzz2BiYoLk5GTs2LEDtWvX1h4M9Ho99u7di5EjR+Lhw4dYu3YtqlatCgA4evQoRo8ejWPHjmHMmDGYOXMmqlSpgvXr18vDgxBCCCGEEBkgNjYWkyZNwrRp0wAAV65cgYWFBfr06YO6devC3d0djRs3RpkyZXDgwAF5O1K8U79+/TB//nxMmDABvXr10pIm0kq7FPHgwYPRqVMnFC9ePKO/qviPkX5KvC96vR6rV6+Gv78/li1bhhw5ciBPnjxISUnBV199hdy5c2Py5Mk4d+4cSpUqBQCIi4uT5C7xThJTQoh/6z+dOPF60sTVq1cxffp0dOnSBfv378cXX3yBUqVKYfLkyahSpQqAP9+0NRxreDBImzzRv39/LFmyBCVLlsTu3bvh4uKSmc0UGextCTaGpInZs2ejTJky2LlzZ7pkCSB9PKY9R3BwMDp16oTbt29j+/bt8PHxSXfMoUOH8OLFCzRo0EAmJIUQf8vx48fh4+MDU1NT9OjRA69evcKmTZvQq1cvmJiYYPr06QgICMCAAQOQnJyMpUuXon///mjVqhV+/vlnrb8iiQMHDmD48OEICwvDjh07kJKSgiFDhuD48ePYuHEjmjZtiiNHjsDNzQ358uXL7KYLIYQQQgiRZURHR2POnDkYM2YMpkyZgsuXL+P69etYuHAhSpUqhaNHj8LJyQmFCxfO7K8qPmKXLl3Cvn370L59e63a4NukTZ4Q4u+Sfkq8L5GRkYiKikKuXLkQFhaG4cOHo3bt2ujUqRMWLFiA6dOn4/fff4ezs7P0VeJvkZgSQvwb/9nEibclTcyYMQOtW7fWssL27NmDFi1aoECBAhg/fjzq1q0LIyMjXL9+HX5+fpg9ezZcXFzeSKJIW3miUKFCuHDhAkxNTTOzuSIDrVq1CkePHoWzszPatGmD3LlzY/r06W9NmoiLi8PPP/+Mjh07wtLS8q3JE6GhoWjQoAHMzc2xadMmuLm5SUk6IcT/5Pjx49i+fTtmzJiB5ORkmJqawt/fHwMGDMDcuXMxYMAAdO/eHbNnz8ayZcswduxY2NjY4NChQ8idOzeAP/sokjh48CCGDh2KW7duIU+ePLh8+TI2bNiAL7/88p3VmoQQQgghhBAfXlRUFObPn48RI0YAALZu3YqGDRvKJLf4RwwvkgnxIUg/Jd4nwzjU8+fPtWWxfX19YWJiggMHDmTytxP/RRJTQoh/4j95x/x3kiYAwMnJCS4uLrh06RI8PT1hZGSEP/74A5UrV8aLFy+glMLcuXPh6OgIvV6vVZ6wsLDAnDlz8Pz5czRu3FiSJrIIkoiOjkZgYCBOnToFkti7dy+8vb2xZMkSlC5dOl3SRExMDCpWrIg//vgDkZGRGDRoELJly5YuPgHgxIkTuHz5MmbOnIlcuXJlVvOEEJ8Aw42+t7c3nJ2dcebMGRw6dAhz5sxBjx49AADdu3fHqlWrcOTIESxatAhTpkyBtbU1Tp48CUdHR23gwpA0oZRC9erVERAQgMmTJ+PIkSOYNGkSvvzyS+j1ekmaEEIIIYQQIhNZW1ujWLFiMDIywqRJk9CwYUOQlMlI8Y9I0oT4kKSfEu+TYRzKwcEBAHDw4EHcvHkTEyZMAPDu5diFeBeJKSHEP/GfqzjxruU52rRpky5p4sSJExgxYgSCgoKwe/du+Pj44PLly6hcuTLs7OyQP39+nDp1CvXr18ecOXO05Im3Ld8hspawsDCYmpri119/xb59+3Dt2jWYm5vj9OnT2j4xMTGoUKEC/vjjD9jZ2YEkRo8ejd69eyNbtmzaxTY6Ohre3t64cuUKgoKCULJkSbkQCyH+Z8HBwfD398f27dsxfvx4DBw4EACQkJAAMzMzfPPNN/j+++8BAB4eHjh9+jQcHR3f+pZR2soTu3btwqRJkxAUFITDhw+jYsWKGdwyIYQQQgghRFovXryAj48PjI2NsX79ehQsWDCzv5IQQqQj/ZT4kHr06IH169cjKCgIXl5emf11xCdAYkoI8Vf+c+nGaZMmLl++jNmzZ6N169YwMzPT9jEkTZw+fRp79+6Fj48PLl26pCVNzJkzBw0bNkSvXr3wyy+/oH///umSJwzJEpI0kfUkJycjd+7cIInHjx/j2LFjePLkCRwdHfHo0SPkyJEDERER8PHxwf379zFz5ky4u7tjwIABmDp1KpRS6NWrl5Y8YWVlhdGjR8PIyAjFixcHAEmaEEL8Y2kTri5duoQZM2Zg7969mDhxIvr37w8ASExM1K6Fbm5uAIDChQvj6NGjyJ49+ztLs6atPFGvXj3Ex8fD399fy8IWQgghhBBCZB57e3usXLkS9+/fl8lIIcRHSfop8aG8evUKL168wNSpU2WCW7wXElNCiP/Lf67iBAD89NNP6N69OypWrIiFCxeiWLFi2mcnTpzAyJEjcfr0aezbtw+VKlXCpUuXULVqVdja2mLatGn48ssvYWRkhISEBPTv3x/r169H3bp1teQJqTSRNRkqjsTGxmLq1KmYPXs2ypYti6JFiyIwMBAuLi7YtWsXOnXqhDt37mDy5Mno2bMnjI2NsWPHDvTq1QtxcXEYNWqUljzxrt8hhBD/xqVLlzBt2jRs27YNHTp0gLu7O3LkyAFXV1fUrFkTKSkp+OmnnzBu3DhYWFjg9OnTcHBw+Fvr2aatPBEdHQ1ra+sMapUQQgghhBDi75IqlkKIj530U+J9SklJgVJKxtTFeyMxJYT4K//JxIkHDx5g5cqVGD16NDp37oxRo0bB09MTJ0+exPDhw3HmzBkcOHAAFStWxIMHD1C0aFHExMRg6dKlaNeuHQAgLi5OW9qjYcOGOHr0KOrWrYu5c+fCxcUlM5snMkHapIlp06Zh9uzZKF26NA4dOgQAGDFiBAICAmBsbIxs2bIhMDAQTZs2hZWVFYDUShV79+5Fr169EBsbCz8/P/Ts2RMmJiaZ2SwhxCfk/PnzGD58OE6ePIkJEyagatWqKF26tPZ5QEAALC0t4efnBxsbG5w6deqdy3O8iwxuCCGEEEIIIYQQQgghhBAiK/pPplS5ublh4MCBmDp1KpYsWYJJkyZhw4YNWqWJvXv3omLFitDr9YiOjkb16tVhZGSE8PBw6PV66PV6LWni7NmziIiIQHR0NDZs2IC5c+dCr9dncgtFRjIkTcTExGDq1KmYM2cOypQpg127dmn7+Pn5aW9tA0Dp0qVhZWWFhIQEAICxsTFq1KiBhQsXwtTUFAsWLMC9e/cypT1CiE9TfHw89u/fj6FDh2LgwIHw9PTE7NmzMWXKFJQoUQJhYWGYMWMGrK2t/1XSBCBLCQkhhBBCCCGEEEIIIYQQImv6+7MpHxkzMzN888030Ov1+Pbbb7FixQqYmZlh79698PHxwb1793D37l1UqVIF3333HczNzTFkyBAkJyejX79+MDU1xdGjRzFq1CgcP34c69evR0REBHx9faVETxaSNmliwoQJmDt3Lj7//HPs3LlTW2rDEBfJycmoWrUqDh8+jBo1auDIkSMoWLAgkpOToZSCmZkZ6tati3z58uH06dOIjo7O5NYJIT4lFStW/H/t3WtwVPX9+PF3NjfZEpIAkVSHi5rAEMYpouU2RBxrRTFSi610KNWxKoMVg/UyEQElJlXAoEbb2JaiRa2jA/KgJJaBBBAKQrgEuRRRHAtBBe00lQRM2Nv/Af9sQbE/xWBA3q8nzMDuOd/D7Ox5cN77+bJz505yc3MBSE9P58477wQgFAoxZ84cEhIS2LBhwwlFE5IkSZIkSZIkSWeq0/qJSmJiIoWFhTQ0NFBRUcFrr73GsGHD2LZtG4MHD+bCCy/kT3/6E/369ePRRx8lEAhw//3306FDB3Jzc3n44YdZs2YNr7zyCtdff70jys9AgUCApqYmpkyZwtNPP83QoUP529/+Fo8mDh48yLBhw6ivr2f27NncdNNNTJkyhbKyMi699FL+/ve/k5ubSyQSAeChhx5i1apVfO973yMrK6s9L03St1BrNBEKhUhOTubw4cO88MILlJeXx7fnyMrKMpqQJEmSJEmSJEn6ChJisVisvRdxPMeLGL4obKivrycajdKzZ0+2bt3KZZddRiQSIRKJMHjwYP7whz9w/vnnU19fz5QpU/jLX/7COeecwwcffMDChQv50Y9+RDQaJSEhwXDiDPTPf/6T888/n+9///usXLmS1NRUAJqamhgyZAh79+7lN7/5DTfeeCMdO3YE4Ne//jXl5eVkZWWxevVqcnJy+PDDD7n11lvZtWsXs2fPpqCgoD0vS9K3yPHuf+FwmN///veUlZWRmJjIxo0bycjIMJqQJEmSJEmSJEn6ik7ZcKLVrl27OHToEBdeeOEXRg2RSITExES2b9/OsGHD6Ny5MzNnziQtLY2f/exnDBkyhN/+9rfxeKK0tJQ5c+Zw8803M3fuXOCLowydGbZv305OTk48mvj0008ZOnQob775JrNmzeKuu+4iKSmJlpaW+GuOjidWrVpF7969+c9//kNzczPZ2dl+piSdkNbvjkgkwr///e/49JrPfqfU1dVxyy23sHnzZhYuXMh1113XTiuWJEmSJEmSJEk6vQXaewH/y+bNm5kwYQL33HMPe/fuPe5rotEoiYmJvPXWWwwfPpzMzEweffRRfvKTnzBixAheeOEF3njjDcaPH8/WrVvp3r07999/PzfeeCPPPfcczzzzTHzahM5c/fr1IzU1lXA4DMDrr7/OwYMHSU5O5tChQ/G/P/o1TzzxBJMmTeLjjz9m2LBh7Nixg4yMDM4++2wAP1OSvrLWOCIcDlNVVUVZWRk1NTXA579TevXqxZgxYwgEAsybN4+NGze2x5IlSZIkSZIkSZJOe6dUOHH08IutW7cya9Ysli9fzvDhw+nevftx3xMIBKivr2fgwIEAPP7449xwww3AkTHmBQUFvPrqq7zxxhtMnjyZ1atXc9555zFlyhR+/vOfM3z4cAKBU+q/Qe2odbz9VVddxRNPPMHFF19MSUkJZWVlNDQ0xF/z2XjiX//6Fz/84Q/ZsGGDnydJJyQcDscnTVRXV/PAAw/w2GOPMWPGDFauXPm512dmZnLHHXdQUlLCokWLKC8v54MPPmiHlUuSJEmSJEmSJJ3eToknvNFoFPjvr2m3bNnCY489xmuvvcYzzzzD1KlTAQiFQse8r/XhdV1dHd27dycSicRHmodCofhD8JSUFHr06MFrr73G5MmTqa6uJjc3l2effZa8vLxv5Bp1+mgNeK655hqmT5/OwIEDKS4upqKi4gvjicLCQj744APGjh1Lc3Nzu61d0unlnXfeYeHChRw+fJikpCRCoRBLly6lsLCQUCjEzJkzqampYf78+TQ1NX3u/R07duTmm29mwoQJvPjii7z00kvtcBWSJEmSJEmSJEmnt6T2XgDAX//6V1JTU7n66qvZuXMns2bNYsmSJVx//fVcdNFF1NbWctFFF5GcnBwfX37llVfSoUMHAAoKCkhJSWHy5MmMHj2aV155hcsuuwyAlStXMmXKFHbu3MnUqVN5/PHHeeKJJxg6dCjBYLAdr1qnqoSEhPi4/CuvvBKA4uJipk+fDsCvfvUrMjMz4/FEUlISTz75JNFolFtvvZWzzjqrHVcv6XTxySefUFxcTHV1NY2NjYwdO5Zly5ZRWFgIHLl/devWjb59+9KjRw86dux43ONkZ2dz++23s2PHDoqKihg0aBD5+fnf5KVIkiRJkiRJkiSd1hJiR++P0Q7WrFnDsGHDSElJYfz48Rw4cICFCxdy++23k5yczGOPPcasWbOYNGkS4XCYP//5zxQWFjJmzBiee+45otEogUCAWCxGTU0NRUVF1NfXU1VVRSQS4d5772XNmjW8+uqr/PjHP+b111/n3HPPJScnpz0vW6eB1ngCYMmSJRQXF1NbW0tJSQnjx4+nc+fOAEQiERITE9tzqZJOQ83NzSxYsIAJEyYwePBgbrrpJkpLS4nFYqxYsYJzzjkn/j20Z88empubyc3NjX8vfdbLL7/M2LFjueGGG5g7dy4dOnRw6yBJkiRJkiRJkqQv4ZQIJyorKykrKyMcDpOSksLMmTOZNGkSTz/9NJMmTeK2226jvLycefPmMW3aNDp16sTy5cvp3r078N8H3LFYjGXLlnHffffx7rvv0qNHD7Zv386CBQsYPXr0MQ/CpS/j6M/M4sWLKS0tZc2aNTz88MMUFRWRnJzcziuUdDprbGxk2rRpPPXUU6SkpHDeeeexfPlysrOz42EgwMSJE6msrGTFihX06tXrmGMc/T1VUFBAXV0dmzdvjm9dJUmSJEmSJEmSpP+t3X6K2tprDB06lF/+8pcMHz6cQCDAU089xaRJkwC47bbbGDhwIK+//jpz5sxh+vTppKWlsXbtWrp3704kEgGO3Vrh8ssvZ9asWQwYMIDt27dTWlrK6NGjiUaj7XWpOo21frYArrrqKu6++2769OlD7969jSYknbDW75W0tDR69+5NZmYmqamplJeXk52d/bnQb+/evezZs4eDBw9+7lgJCQnxe9ygQYP48MMPef755485jyRJkiRJkiRJkr5Yu4UTrQ+EtmzZwkMPPURtbS1lZWWMHz8egJaWFs466yyGDh3K22+/zaRJk+jQoQPr1q2ja9euhMPhY7ZHODqe+MEPfsB9993H4MGDKS4u5o033iAQCDhtQifk6Hhi9OjRLFmyhBtuuKGdVyXpdNV6rwqFQixevJiKigrC4TCNjY1s3ryZ3bt3k5CQcEwQ0alTJzp16kQwGIx/Hx0dRbROphg5ciQAu3fvBvC+J0mSJEmSJEmS9CV84+HE0Q96tm3bRllZGUuWLKGkpIS77roLgMOHD5OamgrAueeeC0Dfvn1Zv359PJpISkr63LGPjieuvvpq7r33Xvr370/nzp1P/oXpW+3oeKJ1ixhJ+qpa71GRSISamhoKCwtpaWlh8eLF5OTkUFxczJgxY3jkkUd4991345OVUlNTCQaDpKenx2OIo6OI3bt389FHHxEKhcjPzyccDvPpp586cUKSJEmSJEmSJOlLSIi101OVbdu2MWPGDBYtWsQvfvELevbsyXe/+12ys7O54ooriEQizJ07l+nTpxMMBqmtraVz585fGE0crfXBVCwWo6mpibS0tG/oqiRJOr7We1M4HKa6uprCwkIAVq1aRbdu3Rg1ahSVlZX07duXHTt2kJGRQZ8+fRg5ciQLFiygsbGRpUuXkpmZSTQaZd++faxcuZK6ujqWLVvGokWLyMvLo6WlhUAg4HZCkiRJkiRJkiRJX9L/LhBOkrq6OoqKili7di0PP/wwl112GQMGDIj/+6xZs/jOd77Dgw8+SKdOnVi7du2Xjibg2MkTRhOSpFNBazRRVVVFSUkJiYmJrFq1iq5duwJwySWXUFlZycSJE+nfvz/l5eWsW7eO0tJSotEokUiEK664goaGhuNOlHjvvffIy8uLT2ySJEmSJEmSJEnSl9Mu4URzczPV1dUUFxdz11138cknn1BeXs7Bgwd5+eWXqa+vp7KykrS0NNatW/c/t+f4Iu7rLkk61TQ0NFBRUcGmTZuYOnVqPJoAyMvLAyAzM5MhQ4YwcOBADhw4wOrVq2loaGDFihXs37+fxsZGkpOTCQaDZGdnM2DAALKzs7nmmmva67IkSZIkSZIkSZJOa+0STgwZMoSdO3eSm5sLQHp6OnfeeScAoVCIOXPmkJCQwIYNG04ompAk6VSUlZXF3XffzZtvvklpaSm9e/dm3LhxAASDQQB27doFHAkAMzMzKSgoAGDcuHEkJCRw6NAhUlJSSExM/Fwk2DptSZIkSZIkSZIkSV9eoL1O3BpNhEIhAA4fPszcuXMpLy8nKSmJDRs2kJWVZTQhSfrWiMVijBgxgnnz5pGfn0/Xrl2JRCLAkUkTAIsWLWL//v0EAgGi0Shw5B7ZGlQEg8F4NHH0Vh3gtCVJkiRJkiRJkqQT8Y2HE599yJOcnEw4HOaPf/wjJSUlpKens2nTJqMJSdK3TmvsMGLECBYuXMjll19OYmIi8N/74/r167n22mvZs2cPgcCR2/Rbb73F2LFjmT9/fvw4R/8pSZIkSZIkSZKkE3dSw4nWh0CRSISPP/4Y4Li/kN26dSvPPvsse/bsYfbs2WRkZAAYTUiSvnVaY4cuXbqQkpISvye+//77AOTk5LBp0yauu+466uvrAaitrWXjxo3s27evfRYtSZIkSZIkSZL0LXbSwonWfdbD4TBVVVWUlZVRU1MDfP4Xsr169WLMmDEEAgHmzZvHxo0bT9ayJEk6pbTeExsbGwGYPHkyDz74IFu2bGHUqFE0NTVx4MABAPr3799ey5QkSZIkSZIkSfrWOikjHVq32IhEIlRXV/PAAw/wj3/8g02bNpGcnMyll156zOszMzO54447iEajTJs2jbS0NGbMmME555xzMpYnSdIpo/WeuX79egDy8vIYPXo0zc3NzJgxg6FDh9LS0sLZZ59Nz54942GiJEmSJEmSJEmS2kabTZx45513WLhwIYcPHyYpKYlQKMTSpUspLCwkFAoxc+ZMampqmD9/Pk1NTZ97f8eOHbn55puZMGECL774Ii+99FJbLU2SpFNW67ZU27Zto3v37lxwwQWkp6dTVFTElClTeP/993nnnXe4+OKL6dGjh9GEJEmSJEmSJElSG2uTiROffPIJxcXFVFdX09jYyNixY1m2bBmFhYUArFy5km7dutG3b1969OhBx44dj3uc7Oxsbr/9dnbs2EFRURGDBg0iPz+/LZYoSdIpq6qqip07d9KlSxeCwSCxWIz09HTuueceampqWLt2Lbm5uQBOnJAkSZIkSZIkSWpjbTJxIjU1lauuuoqmpiZeeOEFXn755Xg0sWLFCrp160YsFqOgoICMjAzefvttYrHYcY/Vr18/brvtNmKxGL/73e84ePAg0Wi0LZYpSdIpKT8/nwULFrBgwYJ4OBGNRsnIyODJJ5/kpz/9KbfccguA0YQkSZIkSZIkSVIbS4h9UcHwFTU2NjJt2jSeeuopUlJSOO+881i+fDnZ2dlEo1ECgSONxsSJE6msrGTFihX06tXrmGMc/SvagoIC6urq2Lx5M1lZWW2xREmSTkuhUIjk5OT2XoYkSZIkSZIkSdK30teeONHaXaSlpdG7d28yMzNJTU2lvLyc7Ozsz40U37t3L3v27OHgwYOfO1ZCQkJ8usSgQYP48MMPef755485jyRJZxqjCUmSJEmSJEmSpJPna4UTrVFEKBRi8eLFVFRUEA6HaWxsZPPmzezevZuEhIRjgohOnTrRqVOn+Cjy1uPEF/T/J1OMHDkSgN27dwOOJpckSZIkSZIkSZIkSW3vhMOJ1mgiEolQU1NDYWEhLS0tLF68mJycHIqLixkzZgyPPPII7777LpFIBIDU1FSCwSDp6enxGOLoKGL37t189NFHhEIh8vPzCYfDfPrpp06ckCRJkiRJkiRJkiRJbS4hdgJFQms0EQ6Hqa6uprCwEIBVq1bRrVs3Ro0aRWVlJX379mXHjh1kZGTQp08fRo4cyYIFC2hsbGTp0qVkZmYSjUbZt28fK1eupK6ujmXLlrFo0SLy8vJoaWkhEAg4olySJEmSJEmSJEmSJJ0USSfyptZooqqqipKSEhITE1m1ahVdu3YF4JJLLqGyspKJEyfSv39/ysvLWbduHaWlpUSjUSKRCFdccQUNDQ3HnSjx3nvvkZeXR2pqattcpSRJkiRJkiRJkiRJ0nGcUDgB0NDQQEVFBZs2bWLq1KnxaAIgLy8PgMzMTIYMGcLAgQM5cOAAq1evpqGhgRUrVrB//34aGxtJTk4mGAySnZ3NgAEDyM7O5pprrvn6VyZJkiRJkiRJkiRJkvR/OOFwIisri7vvvps333yT0tJSevfuzbhx4wAIBoMA7Nq1CzgyoSIzM5OCggIAxo0bR0JCAocOHSIlJYXExEQSEhKOOX7rdiCSJEmSJEmSJEmSJEknS+BE3xiLxRgxYgTz5s0jPz+frl27EolEgCOTJgAWLVrE/v37CQQCRKNRAA4fPhwPKoLBYDyaOHqrDsBoQpIkSZIkSZIkSZIknXQnHE60xg4jRoxg4cKFXH755SQmJgLEI4j169dz7bXXsmfPHgKBI6d66623GDt2LPPnz48f5+g/JUmSJEmSJEmSJEmSviknHE7Af2OHLl26kJKSEg8m3n//fQBycnLYtGkT1113HfX19QDU1tayceNG9u3b93VOLUmSJEmSJEmSJEmS9LV9rXDis1pDisbGRgAmT57Mgw8+yJYtWxg1ahRNTU0cOHAAgP79+7flqSVJkiRJkiRJkiRJkr6ypLY8WDgcJikpifXr1wOQl5fH6NGjaW5uZsaMGQwdOpSWlhbOPvtsevbsSSwWc4sOSZIkSZIkSZIkSZLUbto0nEhKOnK4bdu20b17dy644ALS09MpKioiMTGRiooKGhoauPrqq+nRo0dbnlqSJEmSJEmSJEmSJOkra9OtOgCqqqrYuXMnwWCQYDBILBYjPT2de+65hz59+gCQm5sLQCwWa+vTS5IkSZIkSZIkSZIkfWkJsTauFw4cOMDmzZvp0qUL/fr1IxqNAhAIBKitrWX27NlMnTqVCy+8sC1PK0mSJEmSJEmSJEmS9JW1eTjxfwmFQiQnJ3+Tp5QkSZIkSZIkSZIkSTqubzyckCRJkiRJkiRJkiRJOlUE2nsBkiRJkiRJkiRJkiRJ7cVwQpIkSZIkSZIkSZIknbEMJyRJkiRJkiRJkiRJ0hnLcEKSJEmSJEmSJEmSJJ2xDCckSZIkSZIkSZIkSdIZy3BCkiRJkiRJkiRJkiSdsQwnJEmSJEmSJEmSJEnSGctwQpIkSZIkSZIkSZIknbEMJyRJkiRJkiRJkiRJ0hnLcEKSJEmSJEmSJEmSJJ2xDCckSZIkSZIkSZIkSdIZy3BCkiRJkiRJkiRJkiSdsf4f7R4N4F745cQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#缺失值检查\n",
    "msno.bar(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0296b035-5b13-4aab-a5a4-f24e058b61f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:26.551673Z",
     "start_time": "2023-08-19T14:50:25.265237Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:10.399511Z",
     "iopub.status.busy": "2023-08-25T16:09:10.399511Z",
     "iopub.status.idle": "2023-08-25T16:09:11.748648Z",
     "shell.execute_reply": "2023-08-25T16:09:11.748648Z",
     "shell.execute_reply.started": "2023-08-25T16:09:10.399511Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151080"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#重复数目检查\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37b4f1a0-279b-4d87-ae1c-bf5810ce77c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:28.767158Z",
     "start_time": "2023-08-19T14:50:27.245439Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:11.750661Z",
     "iopub.status.busy": "2023-08-25T16:09:11.749659Z",
     "iopub.status.idle": "2023-08-25T16:09:13.325798Z",
     "shell.execute_reply": "2023-08-25T16:09:13.325798Z",
     "shell.execute_reply.started": "2023-08-25T16:09:11.749659Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#重复值处理进行删除\n",
    "df.drop_duplicates(keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "23b88c8e-22b8-4805-b257-91051c428a39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:30.021424Z",
     "start_time": "2023-08-19T14:50:28.769158Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:13.326801Z",
     "iopub.status.busy": "2023-08-25T16:09:13.326801Z",
     "iopub.status.idle": "2023-08-25T16:09:14.543403Z",
     "shell.execute_reply": "2023-08-25T16:09:14.543403Z",
     "shell.execute_reply.started": "2023-08-25T16:09:13.326801Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查是否删除成功\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b81852a-df7b-40fc-92ff-f0592e65974f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:30.037034Z",
     "start_time": "2023-08-19T14:50:30.022515Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:14.545542Z",
     "iopub.status.busy": "2023-08-25T16:09:14.545542Z",
     "iopub.status.idle": "2023-08-25T16:09:14.559166Z",
     "shell.execute_reply": "2023-08-25T16:09:14.559166Z",
     "shell.execute_reply.started": "2023-08-25T16:09:14.545542Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1951968, step=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#索引重置\n",
    "df.index = range(df.shape[0])\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "754121b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:31.249243Z",
     "start_time": "2023-08-19T14:50:30.039118Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:14.560169Z",
     "iopub.status.busy": "2023-08-25T16:09:14.560169Z",
     "iopub.status.idle": "2023-08-25T16:09:15.812714Z",
     "shell.execute_reply": "2023-08-25T16:09:15.812211Z",
     "shell.execute_reply.started": "2023-08-25T16:09:14.560169Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>农产品市场所在省份</th>\n",
       "      <td>1951968</td>\n",
       "      <td>25</td>\n",
       "      <td>北京</td>\n",
       "      <td>416074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>市场名称映射值</th>\n",
       "      <td>1951968</td>\n",
       "      <td>76</td>\n",
       "      <td>4A0234139A014A1DC38F4A5243106323</td>\n",
       "      <td>350459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>农产品类别</th>\n",
       "      <td>1951968</td>\n",
       "      <td>16</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>1639236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>农产品名称映射值</th>\n",
       "      <td>1951968</td>\n",
       "      <td>2220</td>\n",
       "      <td>550A971AA01208A93858AF5F7B465DE8</td>\n",
       "      <td>41100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>规格</th>\n",
       "      <td>1951968</td>\n",
       "      <td>1</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1951968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区域</th>\n",
       "      <td>1951968</td>\n",
       "      <td>1</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1951968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>颜色</th>\n",
       "      <td>1951968</td>\n",
       "      <td>41</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1946841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>单位</th>\n",
       "      <td>1951968</td>\n",
       "      <td>33</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1946841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最低交易价格</th>\n",
       "      <td>1951968</td>\n",
       "      <td>1664</td>\n",
       "      <td>0.00</td>\n",
       "      <td>410095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均交易价格</th>\n",
       "      <td>1951968</td>\n",
       "      <td>3200</td>\n",
       "      <td>2.00</td>\n",
       "      <td>70129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最高交易价格</th>\n",
       "      <td>1951968</td>\n",
       "      <td>1796</td>\n",
       "      <td>0.00</td>\n",
       "      <td>410076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>数据入库时间</th>\n",
       "      <td>1951968</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-07-18 15:34:18</td>\n",
       "      <td>1946841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>数据发布时间</th>\n",
       "      <td>1951968</td>\n",
       "      <td>2648</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>4364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count unique                               top     freq\n",
       "农产品市场所在省份  1951968     25                                北京   416074\n",
       "市场名称映射值    1951968     76  4A0234139A014A1DC38F4A5243106323   350459\n",
       "农产品类别      1951968     16                                蔬菜  1639236\n",
       "农产品名称映射值   1951968   2220  550A971AA01208A93858AF5F7B465DE8    41100\n",
       "规格         1951968      1                                \\N  1951968\n",
       "区域         1951968      1                                \\N  1951968\n",
       "颜色         1951968     41                                \\N  1946841\n",
       "单位         1951968     33                                \\N  1946841\n",
       "最低交易价格     1951968   1664                              0.00   410095\n",
       "平均交易价格     1951968   3200                              2.00    70129\n",
       "最高交易价格     1951968   1796                              0.00   410076\n",
       "数据入库时间     1951968      2               2016-07-18 15:34:18  1946841\n",
       "数据发布时间     1951968   2648                        2015-12-30     4364"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#异常值处理(显示非数字),都不是数字格式\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0cb5b7cb-e67b-457c-8bb6-b69084048893",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:56.183842Z",
     "start_time": "2023-08-19T14:50:56.065822Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:15.813716Z",
     "iopub.status.busy": "2023-08-25T16:09:15.812714Z",
     "iopub.status.idle": "2023-08-25T16:09:15.920518Z",
     "shell.execute_reply": "2023-08-25T16:09:15.920518Z",
     "shell.execute_reply.started": "2023-08-25T16:09:15.813716Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1 = df.copy()\n",
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9f54a38b-90d9-4772-8627-ebad31befe03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:56.513164Z",
     "start_time": "2023-08-19T14:50:56.400399Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:15.921516Z",
     "iopub.status.busy": "2023-08-25T16:09:15.921516Z",
     "iopub.status.idle": "2023-08-25T16:09:16.029683Z",
     "shell.execute_reply": "2023-08-25T16:09:16.029683Z",
     "shell.execute_reply.started": "2023-08-25T16:09:15.921516Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#对市场名称映射值，农产品名称映射值映射到数字\n",
    "map1, map2 = df['市场名称映射值'].value_counts(), df['农产品名称映射值'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e477a65e-792d-4e91-9a08-a652199eb0cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:56.873353Z",
     "start_time": "2023-08-19T14:50:56.761973Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:16.030683Z",
     "iopub.status.busy": "2023-08-25T16:09:16.030683Z",
     "iopub.status.idle": "2023-08-25T16:09:16.138921Z",
     "shell.execute_reply": "2023-08-25T16:09:16.138921Z",
     "shell.execute_reply.started": "2023-08-25T16:09:16.030683Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2['market'] = df2['市场名称映射值'].map(dict(map1))\n",
    "df2['produce'] = df2['农产品名称映射值'].map(dict(map2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3bd58020-3e0c-4cb3-b552-0531d831e650",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:57.230352Z",
     "start_time": "2023-08-19T14:50:57.090761Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:16.140923Z",
     "iopub.status.busy": "2023-08-25T16:09:16.140923Z",
     "iopub.status.idle": "2023-08-25T16:09:16.265689Z",
     "shell.execute_reply": "2023-08-25T16:09:16.265689Z",
     "shell.execute_reply.started": "2023-08-25T16:09:16.140923Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#删除\n",
    "df2.drop(['市场名称映射值', '农产品名称映射值'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a7b0bd2-1fed-449c-88e4-9b14f942f1aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:58.078974Z",
     "start_time": "2023-08-19T14:50:57.266119Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:16.266802Z",
     "iopub.status.busy": "2023-08-25T16:09:16.266802Z",
     "iopub.status.idle": "2023-08-25T16:09:17.028618Z",
     "shell.execute_reply": "2023-08-25T16:09:17.028114Z",
     "shell.execute_reply.started": "2023-08-25T16:09:16.266802Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#对单位进行提取\n",
    "df2['规格'] = df2['单位'].str.extract('(\\d+)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c4c0b3ea-1296-4f83-8ef2-3749118c2380",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:58.207100Z",
     "start_time": "2023-08-19T14:50:58.079974Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:17.029623Z",
     "iopub.status.busy": "2023-08-25T16:09:17.028618Z",
     "iopub.status.idle": "2023-08-25T16:09:17.151902Z",
     "shell.execute_reply": "2023-08-25T16:09:17.151902Z",
     "shell.execute_reply.started": "2023-08-25T16:09:17.029623Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2['规格'] = df2['规格'].fillna(1)  #规格处对空值用1填补\n",
    "#单位列中\\N改为1\n",
    "df2.loc[df2[\"单位\"].isin([r'\\N']), \"单位\"] = 1\n",
    "#df2.单位.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5dad0ed0-61b9-4972-b9ad-7824a36130d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-26T05:26:42.272056Z",
     "iopub.status.busy": "2023-08-26T05:26:42.272056Z",
     "iopub.status.idle": "2023-08-26T05:26:43.201830Z",
     "shell.execute_reply": "2023-08-26T05:26:43.201830Z",
     "shell.execute_reply.started": "2023-08-26T05:26:42.272056Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df15 = df2['规格']\n",
    "dataset5 = np.asarray(df15, 'str')  #转为str\n",
    "df15 = pd.DataFrame(dataset5, dtype=np.float64)  #转为float\n",
    "df2['规格'] = df15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6635e652-c0db-41eb-a8f0-b659d82348f7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:58.455693Z",
     "start_time": "2023-08-19T14:50:58.208161Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:17.153194Z",
     "iopub.status.busy": "2023-08-25T16:09:17.153194Z",
     "iopub.status.idle": "2023-08-25T16:09:17.371672Z",
     "shell.execute_reply": "2023-08-25T16:09:17.371672Z",
     "shell.execute_reply.started": "2023-08-25T16:09:17.153194Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "map3 = df2['单位'].value_counts()  #对单位映射到数字\n",
    "df2['unit'] = df2['单位'].map(dict(map3))\n",
    "df2.drop(['单位'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9df771fd-4ca1-4055-b1cf-378797388b3f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:58.516656Z",
     "start_time": "2023-08-19T14:50:58.457743Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:17.373235Z",
     "iopub.status.busy": "2023-08-25T16:09:17.372731Z",
     "iopub.status.idle": "2023-08-25T16:09:17.434007Z",
     "shell.execute_reply": "2023-08-25T16:09:17.433505Z",
     "shell.execute_reply.started": "2023-08-25T16:09:17.373235Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "北京     416074\n",
       "湖北     195617\n",
       "安徽     181457\n",
       "新疆     159777\n",
       "湖南     140043\n",
       "山东     128681\n",
       "内蒙古    103143\n",
       "陕西      78142\n",
       "天津      73377\n",
       "江西      69617\n",
       "云南      64558\n",
       "上海      56123\n",
       "浙江      49259\n",
       "江苏      45218\n",
       "福建      38084\n",
       "辽宁      37801\n",
       "四川      36907\n",
       "山西      35553\n",
       "甘肃      26516\n",
       "河南       6934\n",
       "广西       3304\n",
       "河北       3249\n",
       "黑龙江      2077\n",
       "宁夏        413\n",
       "广东         44\n",
       "Name: 农产品市场所在省份, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['农产品市场所在省份'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c7e7a2f8-1ef9-4727-97ac-d309ab23bbbd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:58.594188Z",
     "start_time": "2023-08-19T14:50:58.518153Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:17.435008Z",
     "iopub.status.busy": "2023-08-25T16:09:17.435008Z",
     "iopub.status.idle": "2023-08-25T16:09:17.495394Z",
     "shell.execute_reply": "2023-08-25T16:09:17.495394Z",
     "shell.execute_reply.started": "2023-08-25T16:09:17.435008Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#对区域按中国四大地理区分类(南方地区1)(北方地区2)(西北地区3)(青藏地区4)\n",
    "df2['区域'] = df2['农产品市场所在省份']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0f36b1b-742b-4019-a3cf-86866d570705",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:58.783168Z",
     "start_time": "2023-08-19T14:50:58.595228Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:17.496394Z",
     "iopub.status.busy": "2023-08-25T16:09:17.496394Z",
     "iopub.status.idle": "2023-08-25T16:09:17.650323Z",
     "shell.execute_reply": "2023-08-25T16:09:17.649819Z",
     "shell.execute_reply.started": "2023-08-25T16:09:17.496394Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2.loc[df2[\"区域\"].\n",
    "        isin(['北京', '湖北', '山东', '天津', '辽宁', '山西', '甘肃', '河南', '河北', '黑龙江']),\n",
    "        \"区域\"] = 2\n",
    "df2.loc[df2[\"区域\"].isin(\n",
    "    ['安徽', '湖南', '江西', '云南', '上海', '浙江', '江苏', '福建', '四川', '广西', '广东']),\n",
    "        \"区域\"] = 1\n",
    "df2.loc[df2[\"区域\"].isin(['新疆', '内蒙古', '宁夏', '陕西']), \"区域\"] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e9d33e49-65a8-42ba-a041-2f63c81702ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:58.860934Z",
     "start_time": "2023-08-19T14:50:58.784237Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:17.652332Z",
     "iopub.status.busy": "2023-08-25T16:09:17.652332Z",
     "iopub.status.idle": "2023-08-25T16:09:17.712043Z",
     "shell.execute_reply": "2023-08-25T16:09:17.712043Z",
     "shell.execute_reply.started": "2023-08-25T16:09:17.652332Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    925879\n",
       "1    684614\n",
       "3    341475\n",
       "Name: 区域, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.区域.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0986b88d-275a-4f5d-b239-02a08d479eb9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:58.985621Z",
     "start_time": "2023-08-19T14:50:58.862021Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:17.713134Z",
     "iopub.status.busy": "2023-08-25T16:09:17.713134Z",
     "iopub.status.idle": "2023-08-25T16:09:17.837511Z",
     "shell.execute_reply": "2023-08-25T16:09:17.837511Z",
     "shell.execute_reply.started": "2023-08-25T16:09:17.713134Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1946841\n",
       "各色         1197\n",
       "绿色          790\n",
       "黄色          500\n",
       "白色          389\n",
       "红色          333\n",
       "紫色          233\n",
       "粉色          216\n",
       "粉红色         182\n",
       "桃红色          91\n",
       "金黄色          89\n",
       "黄绿色          87\n",
       "淡粉色          62\n",
       "红黄色          58\n",
       "紫红色          58\n",
       "绿白色          57\n",
       "幻彩色          56\n",
       "乳白色          32\n",
       "黑红色          32\n",
       "粉白色          31\n",
       "橙红色          31\n",
       "深紫色          31\n",
       "浅紫色          31\n",
       "花边色          31\n",
       "蓝色           31\n",
       "橙色           31\n",
       "香槟色          31\n",
       "金红色          31\n",
       "黄边红色         31\n",
       "杏红色          31\n",
       "深红色          31\n",
       "流星点色         31\n",
       "黄底红边         31\n",
       "白黄双色         29\n",
       "黄白绿          29\n",
       "红粉白          29\n",
       "绿红色          29\n",
       "银白色          29\n",
       "褐色           29\n",
       "白/紫          29\n",
       "黄叶白          28\n",
       "Name: 颜色, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#颜色列中\\N改为0\n",
    "df2.loc[df2[\"颜色\"].isin([r'\\N']), \"颜色\"] = 0\n",
    "df2.颜色.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a9fccfd2-aca4-422b-ac24-1b824568db20",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:59.218078Z",
     "start_time": "2023-08-19T14:50:58.988069Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:17.839014Z",
     "iopub.status.busy": "2023-08-25T16:09:17.837511Z",
     "iopub.status.idle": "2023-08-25T16:09:18.055146Z",
     "shell.execute_reply": "2023-08-25T16:09:18.055146Z",
     "shell.execute_reply.started": "2023-08-25T16:09:17.839014Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              333\n",
       "1              333\n",
       "2              216\n",
       "3               32\n",
       "4              216\n",
       "            ...   \n",
       "1951963    1946841\n",
       "1951964    1946841\n",
       "1951965    1946841\n",
       "1951966    1946841\n",
       "1951967    1946841\n",
       "Name: color, Length: 1951968, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#颜色映射到数字\n",
    "map4 = df2['颜色'].value_counts()\n",
    "df2['color'] = df2['颜色'].map(dict(map4))\n",
    "df2.drop(['颜色'], axis=1, inplace=True)\n",
    "df2['color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4deb0b99-ec1a-4533-94c6-9f3a727077ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:59.296072Z",
     "start_time": "2023-08-19T14:50:59.219121Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:18.056147Z",
     "iopub.status.busy": "2023-08-25T16:09:18.056147Z",
     "iopub.status.idle": "2023-08-25T16:09:18.132146Z",
     "shell.execute_reply": "2023-08-25T16:09:18.132146Z",
     "shell.execute_reply.started": "2023-08-25T16:09:18.056147Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00     410076\n",
       "4.00      74231\n",
       "3.00      70260\n",
       "5.00      65047\n",
       "2.00      64359\n",
       "          ...  \n",
       "42.40         1\n",
       "28.40         1\n",
       "6.89          1\n",
       "20.25         1\n",
       "1.01          1\n",
       "Name: 最高交易价格, Length: 1796, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#处理最高交易价格\n",
    "df2['最高交易价格'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f29faae4-07eb-45a6-b64c-858e8811f5b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:50:59.312047Z",
     "start_time": "2023-08-19T14:50:59.297164Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:18.661522Z",
     "iopub.status.busy": "2023-08-25T16:09:18.661522Z",
     "iopub.status.idle": "2023-08-25T16:09:18.678744Z",
     "shell.execute_reply": "2023-08-25T16:09:18.678240Z",
     "shell.execute_reply.started": "2023-08-25T16:09:18.661522Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df3 = df2['最高交易价格']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "841c76b1-fc96-4776-b2a7-4300d80a0386",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:01.523221Z",
     "start_time": "2023-08-19T14:50:59.314251Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:20.255556Z",
     "iopub.status.busy": "2023-08-25T16:09:20.255556Z",
     "iopub.status.idle": "2023-08-25T16:09:22.513856Z",
     "shell.execute_reply": "2023-08-25T16:09:22.513856Z",
     "shell.execute_reply.started": "2023-08-25T16:09:20.255556Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00     442855\n",
       "4.00      74313\n",
       "3.00      70395\n",
       "5.00      65283\n",
       "2.00      64420\n",
       "          ...  \n",
       "24.02         1\n",
       "24.63         1\n",
       "23.87         1\n",
       "24.56         1\n",
       "1.01          1\n",
       "Name: 最高交易价格, Length: 1614, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = np.asarray(df3, 'str')  #转为str\n",
    "dataset = np.core.defchararray.replace(dataset, '（个）', '')  #删除分号\n",
    "df3 = dataset\n",
    "df3 = pd.DataFrame(df3, dtype=np.float64)  #转为float\n",
    "df2['最高交易价格'] = df3\n",
    "#改正之后增加了一点数据\n",
    "df2['最高交易价格'].value_counts()\n",
    "#df3.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ed042780-ffb3-4ec9-b473-b0bd968ba43a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:22.514934Z",
     "iopub.status.busy": "2023-08-25T16:09:22.514934Z",
     "iopub.status.idle": "2023-08-25T16:09:22.576600Z",
     "shell.execute_reply": "2023-08-25T16:09:22.576600Z",
     "shell.execute_reply.started": "2023-08-25T16:09:22.514934Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00     442855\n",
       "4.00      74313\n",
       "3.00      70395\n",
       "5.00      65283\n",
       "2.00      64420\n",
       "          ...  \n",
       "16.37         1\n",
       "16.42         1\n",
       "16.53         1\n",
       "16.55         1\n",
       "17.72         1\n",
       "Length: 1614, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "63c9b6c7-5ef5-456c-9b0f-797530edccfd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T15:14:27.942352Z",
     "iopub.status.busy": "2023-08-25T15:14:27.942352Z",
     "iopub.status.idle": "2023-08-25T15:14:27.950867Z",
     "shell.execute_reply": "2023-08-25T15:14:27.950867Z",
     "shell.execute_reply.started": "2023-08-25T15:14:27.942352Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#样条插值法\n",
    "from scipy import interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "598dc61e-b296-4c0d-99fc-a34be2685ae9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T15:21:59.393063Z",
     "iopub.status.busy": "2023-08-25T15:21:59.393063Z",
     "iopub.status.idle": "2023-08-25T15:21:59.409445Z",
     "shell.execute_reply": "2023-08-25T15:21:59.409445Z",
     "shell.execute_reply.started": "2023-08-25T15:21:59.393063Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data10 = df3\n",
    "data10.columns = ['a']\n",
    "data12 = data10[data10['a'].isin([0])]\n",
    "data10 = data10[~data10['a'].isin([0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "12149c35-d4a0-4e41-b774-d0c27fa1cbe5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T15:14:32.682794Z",
     "iopub.status.busy": "2023-08-25T15:14:32.682794Z",
     "iopub.status.idle": "2023-08-25T15:14:32.854172Z",
     "shell.execute_reply": "2023-08-25T15:14:32.854172Z",
     "shell.execute_reply.started": "2023-08-25T15:14:32.682794Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24.],\n",
       "       [24.],\n",
       "       [24.],\n",
       "       ...,\n",
       "       [24.],\n",
       "       [24.],\n",
       "       [24.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据准备\n",
    "X = data10.index\n",
    "Y = data10['a'].values\n",
    "#x = np.arange(0, len(data10), 0.15)\n",
    "tck = interpolate.splrep(X, Y, s=0)\n",
    "xnew = data12\n",
    "ynew = interpolate.splev(xnew, tck, der=0)\n",
    "ynew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5c0483a5-e527-489a-ab84-a6812273dfb7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:01.585650Z",
     "start_time": "2023-08-19T14:51:01.540269Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:37.998946Z",
     "iopub.status.busy": "2023-08-25T16:09:37.998946Z",
     "iopub.status.idle": "2023-08-25T16:09:38.047254Z",
     "shell.execute_reply": "2023-08-25T16:09:38.047254Z",
     "shell.execute_reply.started": "2023-08-25T16:09:37.998946Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.951968e+06\n",
       "mean     1.176003e+01\n",
       "std      1.626441e+01\n",
       "min      1.200000e-01\n",
       "25%      3.000000e+00\n",
       "50%      6.000000e+00\n",
       "75%      2.400000e+01\n",
       "max      8.000000e+02\n",
       "Name: 最高交易价格, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#最高交易价格用插值法得出的数据填充0值\n",
    "df2.loc[df2[\"最高交易价格\"].isin([0.00]), \"最高交易价格\"] = 24  #8.17（是用均值得出）\n",
    "df2.最高交易价格.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "74f1d1bd-b783-4465-8686-1eedb49444dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:01.647401Z",
     "start_time": "2023-08-19T14:51:01.587768Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:44.217471Z",
     "iopub.status.busy": "2023-08-25T16:09:44.217471Z",
     "iopub.status.idle": "2023-08-25T16:09:44.272986Z",
     "shell.execute_reply": "2023-08-25T16:09:44.272986Z",
     "shell.execute_reply.started": "2023-08-25T16:09:44.217471Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.00     70129\n",
       "3.00     67662\n",
       "4.00     64066\n",
       "5.00     55502\n",
       "6.00     45468\n",
       "         ...  \n",
       "41.86        1\n",
       "41.45        1\n",
       "18.66        1\n",
       "18.71        1\n",
       "41.84        1\n",
       "Name: 平均交易价格, Length: 3200, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#平均交易价格\n",
    "df4 = df2['平均交易价格']\n",
    "df4.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4370a112-94ce-44a1-92f2-b312b01e2496",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:03.859085Z",
     "start_time": "2023-08-19T14:51:01.648860Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:09:45.143815Z",
     "iopub.status.busy": "2023-08-25T16:09:45.143815Z",
     "iopub.status.idle": "2023-08-25T16:09:47.408692Z",
     "shell.execute_reply": "2023-08-25T16:09:47.408190Z",
     "shell.execute_reply.started": "2023-08-25T16:09:45.143815Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1951968"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1 = np.asarray(df4, 'str')  #转为str\n",
    "dataset1 = np.core.defchararray.replace(dataset1, '（个）', '')  #删除\n",
    "df5 = dataset1\n",
    "df5 = pd.DataFrame(df5, dtype=np.float64)  #转为float\n",
    "df2['平均交易价格'] = df5\n",
    "#也一样增加了\n",
    "df2['平均交易价格'].value_counts().sum()\n",
    "#df5.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1c0d6f5a-b06d-424e-8a31-ecbad211b598",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T15:41:33.402003Z",
     "iopub.status.busy": "2023-08-25T15:41:33.402003Z",
     "iopub.status.idle": "2023-08-25T15:41:33.418028Z",
     "shell.execute_reply": "2023-08-25T15:41:33.418028Z",
     "shell.execute_reply.started": "2023-08-25T15:41:33.402003Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data13' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdata13\u001b[49m\u001b[38;5;241m.\u001b[39mmean()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data13' is not defined"
     ]
    }
   ],
   "source": [
    "data13.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e77e8ab6-9ef0-40a2-8d40-23b0cd4ac48b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:03.874602Z",
     "start_time": "2023-08-19T14:51:03.860085Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T15:26:01.408712Z",
     "iopub.status.busy": "2023-08-25T15:26:01.407355Z",
     "iopub.status.idle": "2023-08-25T15:26:01.418942Z",
     "shell.execute_reply": "2023-08-25T15:26:01.418942Z",
     "shell.execute_reply.started": "2023-08-25T15:26:01.408712Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.504432\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j = df5.mean()\n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f7f72d0b-9177-4b48-ac59-9776eee7a16b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:03.922220Z",
     "start_time": "2023-08-19T14:51:03.877105Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:06.651847Z",
     "iopub.status.busy": "2023-08-25T16:10:06.650842Z",
     "iopub.status.idle": "2023-08-25T16:10:06.702113Z",
     "shell.execute_reply": "2023-08-25T16:10:06.702113Z",
     "shell.execute_reply.started": "2023-08-25T16:10:06.651847Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.951968e+06\n",
       "mean     7.508912e+00\n",
       "std      1.624709e+01\n",
       "min      4.000000e-02\n",
       "25%      2.100000e+00\n",
       "50%      3.800000e+00\n",
       "75%      7.080000e+00\n",
       "max      7.200000e+02\n",
       "Name: 平均交易价格, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[df2[\"平均交易价格\"].isin([0]), \"平均交易价格\"] = 7.50  #0值用均值填充\n",
    "df2.平均交易价格.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "37571568-3d15-4faa-8fd8-bd05e2e27f90",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:03.937862Z",
     "start_time": "2023-08-19T14:51:03.923286Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:14.105834Z",
     "iopub.status.busy": "2023-08-25T16:10:14.105834Z",
     "iopub.status.idle": "2023-08-25T16:10:14.116920Z",
     "shell.execute_reply": "2023-08-25T16:10:14.116417Z",
     "shell.execute_reply.started": "2023-08-25T16:10:14.105834Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#最低交易价格\n",
    "df6 = df2['最低交易价格']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d739836d-8ecb-4370-8242-1bfc1b005ca4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:06.128478Z",
     "start_time": "2023-08-19T14:51:03.938863Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:14.492877Z",
     "iopub.status.busy": "2023-08-25T16:10:14.492877Z",
     "iopub.status.idle": "2023-08-25T16:10:16.727406Z",
     "shell.execute_reply": "2023-08-25T16:10:16.727406Z",
     "shell.execute_reply.started": "2023-08-25T16:10:14.492877Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00     442864\n",
       "2.00      89766\n",
       "3.00      76202\n",
       "4.00      70442\n",
       "1.00      51186\n",
       "          ...  \n",
       "3.11          1\n",
       "10.08         1\n",
       "24.22         1\n",
       "23.16         1\n",
       "60.70         1\n",
       "Name: 最低交易价格, Length: 1488, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2 = np.asarray(df6, 'str')  #转为str\n",
    "dataset2 = np.core.defchararray.replace(dataset2, '（个）', '')  #删除分号\n",
    "df7 = dataset2\n",
    "df7 = pd.DataFrame(df7, dtype=np.float64)  #转为float\n",
    "df2['最低交易价格'] = df7\n",
    "df2.最低交易价格.value_counts()\n",
    "#df7.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1391ec85-e08c-4aa8-ae4f-0c76078b0411",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T15:43:08.636979Z",
     "iopub.status.busy": "2023-08-25T15:43:08.636979Z",
     "iopub.status.idle": "2023-08-25T15:43:08.653584Z",
     "shell.execute_reply": "2023-08-25T15:43:08.653584Z",
     "shell.execute_reply.started": "2023-08-25T15:43:08.636979Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    6.767447\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data14 = df7\n",
    "data14.columns = ['a']\n",
    "data14 = data14[~data14['a'].isin([0])]\n",
    "data14.value_counts().sum()\n",
    "data14.mean()  #得出均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "034a18e1-d861-4e84-9757-89520b6ca040",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T15:11:42.351196Z",
     "iopub.status.busy": "2023-08-25T15:11:42.351196Z",
     "iopub.status.idle": "2023-08-25T15:11:42.548181Z",
     "shell.execute_reply": "2023-08-25T15:11:42.548181Z",
     "shell.execute_reply.started": "2023-08-25T15:11:42.351196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24.],\n",
       "       [24.],\n",
       "       [24.],\n",
       "       ...,\n",
       "       [24.],\n",
       "       [24.],\n",
       "       [24.]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据准备\n",
    "X = data10.index\n",
    "Y = data10['a'].values\n",
    "#x = np.arange(0, len(data10), 0.15)\n",
    "tck = interpolate.splrep(X, Y, s=0)\n",
    "xnew = data12\n",
    "ynew = interpolate.splev(xnew, tck, der=0)\n",
    "ynew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3552a3f3-5b22-4dd7-8a59-46630a288c72",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:06.192076Z",
     "start_time": "2023-08-19T14:51:06.145497Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:24.109017Z",
     "iopub.status.busy": "2023-08-25T16:10:24.109017Z",
     "iopub.status.idle": "2023-08-25T16:10:24.154966Z",
     "shell.execute_reply": "2023-08-25T16:10:24.154966Z",
     "shell.execute_reply.started": "2023-08-25T16:10:24.109017Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.951968e+06\n",
       "mean     9.542778e+00\n",
       "std      1.395722e+01\n",
       "min      2.000000e-02\n",
       "25%      2.000000e+00\n",
       "50%      4.800000e+00\n",
       "75%      1.900000e+01\n",
       "max      5.400000e+02\n",
       "Name: 最低交易价格, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#最高交易价格用插值法得出的数据填充0值\n",
    "df2.loc[df2[\"最低交易价格\"].isin([0.00]), \"最低交易价格\"] = 19  #6.78均值\n",
    "df2.最低交易价格.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "73041b5a-810b-4f86-9897-674d3d6f7b10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:06.332191Z",
     "start_time": "2023-08-19T14:51:06.193077Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:27.360869Z",
     "iopub.status.busy": "2023-08-25T16:10:27.360869Z",
     "iopub.status.idle": "2023-08-25T16:10:27.507583Z",
     "shell.execute_reply": "2023-08-25T16:10:27.507583Z",
     "shell.execute_reply.started": "2023-08-25T16:10:27.360869Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#修改一下列表\n",
    "df2['low_price'] = df2['最低交易价格']\n",
    "df2['avg_price'] = df2['平均交易价格']\n",
    "df2['high_price'] = df2['最高交易价格']\n",
    "df2.drop(['最低交易价格', '平均交易价格', '最高交易价格'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "abf21a0d-952b-47b8-a484-db0c1273578a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T14:51:06.347796Z",
     "start_time": "2023-08-19T14:51:06.333191Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:27.516656Z",
     "iopub.status.busy": "2023-08-25T16:10:27.516656Z",
     "iopub.status.idle": "2023-08-25T16:10:27.538594Z",
     "shell.execute_reply": "2023-08-25T16:10:27.538594Z",
     "shell.execute_reply.started": "2023-08-25T16:10:27.516656Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>农产品市场所在省份</th>\n",
       "      <th>农产品类别</th>\n",
       "      <th>规格</th>\n",
       "      <th>区域</th>\n",
       "      <th>数据入库时间</th>\n",
       "      <th>数据发布时间</th>\n",
       "      <th>market</th>\n",
       "      <th>produce</th>\n",
       "      <th>unit</th>\n",
       "      <th>color</th>\n",
       "      <th>low_price</th>\n",
       "      <th>avg_price</th>\n",
       "      <th>high_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>云南</td>\n",
       "      <td>玫瑰花</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-07-18 15:05:19</td>\n",
       "      <td>2015-11-27</td>\n",
       "      <td>5127</td>\n",
       "      <td>32</td>\n",
       "      <td>1170</td>\n",
       "      <td>333</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  农产品市场所在省份 农产品类别    规格 区域               数据入库时间      数据发布时间  market  produce  \\\n",
       "0        云南   玫瑰花  20.0  1  2016-07-18 15:05:19  2015-11-27    5127       32   \n",
       "\n",
       "   unit  color  low_price  avg_price  high_price  \n",
       "0  1170    333       19.0       22.0        24.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a0fe2e5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:04:35.457912Z",
     "start_time": "2023-08-19T16:04:35.434333Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:27.665160Z",
     "iopub.status.busy": "2023-08-25T16:10:27.664157Z",
     "iopub.status.idle": "2023-08-25T16:10:27.693868Z",
     "shell.execute_reply": "2023-08-25T16:10:27.693868Z",
     "shell.execute_reply.started": "2023-08-25T16:10:27.665160Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.00    445464\n",
       "2.00      89766\n",
       "3.00      76202\n",
       "4.00      70442\n",
       "1.00      51186\n",
       "          ...  \n",
       "3.11          1\n",
       "10.08         1\n",
       "24.22         1\n",
       "23.16         1\n",
       "60.70         1\n",
       "Name: low_price, Length: 1487, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.low_price.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d572b2af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T15:28:20.345659Z",
     "start_time": "2023-08-19T15:28:20.137538Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:27.802603Z",
     "iopub.status.busy": "2023-08-25T16:10:27.801572Z",
     "iopub.status.idle": "2023-08-25T16:10:28.085338Z",
     "shell.execute_reply": "2023-08-25T16:10:28.085338Z",
     "shell.execute_reply.started": "2023-08-25T16:10:27.802603Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6VEajUU2bNlVWVlb4/aV69epp1qxZmjNnjpo3b67XXntNZ5xxRvh9pGgUFxcrLy8v/I5T/fr1FQgE5PV69dlnn4XHnDJliho1ahTu16ZNG73//vslxpEOPYo2cOBAtW3bVmeccUaJRwrLeq/npZdektvt1tKlS9W0aVN9+umneuSRR3TvvfeG51u3bp1GjRoV7vP+++/rt99+05w//G/o9XpLbSBxWGFhoTIyMjR48OAS4WjcuHEaN26cQqGQfD6f3n//fV1wwQXhfn6/v9wVp7Ler/L5fCouLta8efO0bt06ff755zKZTFq1apX++c9/6qKLLtLFF19cZp3VzRCKtC54nPF4PHI4HHK73SVekAMAAED5ioqK9Pvvv+vEE08s8S2dbdu2lXgMrqY5nU6lpqZG3c/v92vdunURd18LBoOlNlM4VgSDQRkMhoiPBv6Zy+WqVBiNxuFHDY+0wUUkgUAg4kd6jzRvWf/eStFlA1acAAAAUCWpqamVCjKxZjKZKrRltdFoPCZDk1T5DSdqOjRJKhVaolWZ0FSd2FUPAAAAACIgOAEAAABABAQnAAAARKWOvSKPY1x1/fvKO04AAACokPj4eBkMBu3bt0+NGzeOegMCINZCoZD27dsng8Gg+Pj4Ko1FcAIAAECFxMXFqUWLFtq+fbu2bNlS2+UAFWIwGNSiRYsqby5BcAIAAECF2e12nXzyyaU+2AocreLj46tlRz6CEwAAAKISFxdX61tDA7HG5hAAAAAAEEGtBaeff/5Zy5cvr3B7t9uttWvXVtv8u3fvrraxAAAAABzfai04ffXVVxo6dKiCwWCF2s+YMUMDBgxQUVFRtcxPcAIAAABQUbUWnIYNG6YWLVqUCDCBQKDMYOT1ejV58mRNnTpVVqs1lmUCAAAAQOw2h2jWrJn27t1bar//1NTU8J+DwaBCoZAKCws1evRoffDBB5IOBaq8vDzdeOONJfoGAgEVFxdrw4YNatmyZc3fBAAAAIA6KWYrTvXq1dPnn38uv99/xCMYDMrn88lqter5559Xbm6uNm7cKIvFogULFignJ0c7duzQgQMHdODAAXk8Hu3fv79E+Pozr9crj8dT4gAAAACAaMQsONlstgrt93/4i75ms1kGg0EjRozQlVdeqb/+9a/64osv1Lp163Bbg8GghISEcr9anZaWJofDET5SUlKqfjMAAAAA6pSYvuNU0Y0gJMnn8+mee+7R2rVr9Z///EeSZLVaZTL97+nCUCgkr9db7jhjx46V2+0OH9nZ2ZUrHgAAAECdFbN3nILBYLkrQ3+Wn5+v1157TfXr11fnzp0lSYWFhdqzZ4/atGkj6VC4MhqN2rJlyxHHsVgsslgsVSkdAAAAQB0Xs+CUk5OjhISECrdv2LCh3G63QqFQ+MvUixYt0pAhQ7Rp06aaKhMAAAAASonJo3qFhYVyuVxq0aJFVP2MRqPGjx+vYcOGlbo2fvx4PfTQQ9VVIgAAAAAcUUxWnL799lslJCSU2NihIt577z1NnTpVc+fOLXXtyiuvVK9evXTw4EE9++yzUdd0wgknRN0HAAAAQN1U48EpFApp0qRJuuKKK8I75lXEO++8o5EjR+rjjz9Wr169JEkHDhwIvyfVpUsXff311+rZs6dsNpueeuqpqOoiOAEAAACoqBoPTkuWLNGyZcv0/PPPR9Vv1apVmjVrli699FJJ0uWXX66FCxfq2muvDbfp3Lmz5s2bpwYNGlRnyQAAAABQgiEUCoVqepKdO3cqOTm5SmOsX79eRqNRbdu2rdI4Ho9HDodDbrdbiYmJVRoLAAAAwLErmmwQk3ecqhqaJKl9+/bVUAkAAAAARC+mH8AFAAAAgGMRwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAgITgAAAAAQAcEJAAAAACIgOAEAAABABAQnAAAAAIiA4AQAAAAAEdTZ4JSdnV3bJQAAAAA4RtRacAoEAke8FgwG5fV6FQqFIo7j9/vl9Xqjnv/0bt20bdu2qPsBAAAAqHtMtTGp3+/XGWecoQceeEB/+9vfSl1fvXq1rr32WlmtVsXFxUk6FLQyMzPVvn17xcfHh9t6vV516dJF7777blQ1eIuK5HK5lJqaWrWbAQAAAHDcq5XgZDKZNHjwYM2ePbvM4NStWzdt3LhRK1eu1Lp16yRJ69ev19atW3XvvffKYDCE2/bs2VOtWrWKVekAAAAA6qCYBKcTTjhB+/btKxF4wgWY/ldCKBSS0+nUnj17JEkfffSRfvzxRw0ZMkTt27fXs88+W6LvxIkT1bBhQ4ITAAAAgBoVk+AUHx+v119/XUOHDi233ddff61bbrmlRL+1a9fq+eefL7P93r17Szy2Vxav11viHSiPx1PxwgEAAABAMQpOd9xxhzp37hyxXfPmzTVgwIDwb6PRqC5duujmm28us/1DDz0ko7H8/S3S0tI0ceLEaMoFAAAAgBJiEpxeffVVPf7447JYLOFzPp9PoVCo1LkTTzwx/DsYDMrn8+nAgQNljuv3+xUMBsude+zYsRozZkz4t8fjUUpKSiXvBAAAAEBdFJPgtHnz5lLnJkyYoE2bNmnmzJlH7Ge1WrVz50499dRTMplMcjgcJa4bjcYy35v6I4vFUiKcAQAAAEC0amVXvYp66KGH1KdPH11wwQVauHChzjrrrNouCQAAAEAdVGsfwK2IRYsW6eKLL1ZxcbHuuOMOdevWTd26dVOnTp1kMBj08MMP13aJAAAAAOqAozY4PfbYY7r66qv1+OOP6/TTT9fFF1+sZcuWadGiRUpJSdGIESP06KOPVnp8i9Uqp9NZjRUDAAAAOF7VSnAKBoPKy8sr9/2km266ST/99JNGjhypr7/+Wm63W3/5y1902mmn6ayzztK0adOqVMOqH39UampqlcYAAAAAUDfE/B2n3NxcnXDCCSouLtbUqVPLbHPgwAFt2rRJe/bs0aZNm7RmzRotXbpUPXv2VHx8vF555RUtW7ZMHTp0UOPGjdW4cWNdd911MpvNFa6DnfUAAAAAVFTMg1PDhg21YcMGNW3aVDabrcw2BoNB999/v5o3b64OHTroxhtv1PTp01W/fn1Jhz5qu3jxYv3444/KzMxUampqVKEJAAAAAKJhCIVCodouIpY8Ho8cDofcbrcSExNruxwAAAAAtSSabHDUbg4BAAAAAEcLghMAAAAAREBwAgAAAIAICE4AAAAAEAHBCQAAAAAiIDgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIjDVxqTff/+9GjZsqHbt2tXG9JKkjIwM2e12SZLT6VRqamqt1QIAAADg6BZ1cMrOzlbLli3Vvn378Lm9e/dKkpo0aRI+t2HDBq1Zs0YdO3YsNcb06dOVk5Ojjz76SOvXr9err76qp59+WkZjyQWwf//739q6dasee+wx2Ww2+Xw+WSwWxcXFhdsUFRUpLy9PDRs2LHE+kh49eoT/bLXZtCEri/AEAAAAoExRP6pnsVhkNpuVkZGhiRMn6ueff9bw4cM1fPhw/fTTTxo/frxWr16tpKQkWSyWMsewWq3q0KGDpENha/78+frXv/5Vql3fvn31ww8/qGvXrnrnnXdUr149mUwmGQyG8GGz2dS4cWP9/vvv0d1Iv37S8OHSVVepqLBQLpcr2r8KAAAAAHVE1CtO8fHxkiSv16uRI0eWWOV56623NG7cOF122WWHBjcdGj4UCslgMITbFRYW6qSTTpJ06DG5jz76SAUFBQoGgyoqKlJCQoIk6ZRTTtHixYu1ZMkSnX/++RowYIDMZrO+//573XTTTdqyZYuKi4vl9XqVmJgY3Y0kJUnJydHePgAAAIA6KOrg5PP5JEn16tXTww8/rIMHD4aveb1e/fvf/1b9+vUlScXFxZKkE044QQUFBeEVKI/HI4vForFjx8pms4VDVV5eni644AJ99dVX2rt3rxwOhywWiy6++GJJktlslqRwWDOZTDKZTLLZbJW6eQAAAACoiKiDk9/vl91ul9PpDJ8rKCiQpPBK0R133CFJCgQCkqQ9e/aE24ZCITVu3FiffvqprrrqKi1btkytWrUqNc/DDz+szz//XA8//LBuueWW8OpVtLxer7xeb/i3x+Op1DgAAAAA6q6o00jz5s1LvQ80btw4SdJjjz0Wsf8vv/yivLw8denSpcT5Rx55RKeffrr69OkjSfrPf/6j1NRUPfLIIxo0aJAaNmwYbamSpLS0NE2cOLFSfQEAAABAqkRwcrvdatmypSwWS/h9J4/HI6vVqrlz50o6tCpVUFCg77//Xi1atCjR/5NPPtHFF19c4vG6NWvW6KmnntKiRYvC5xISEvTQQw9p9OjR4Uf/KmPs2LEaM2ZM+LfH41FKSkqlxwMAAABQ90QdnBITE5WdnS273R5+N+nPK06hUEj5+fnhR/cOy8vL07///W+99dZb4XOFhYUaOnSonnjiCZ1xxhml5tu8ebPee+89TZgwoVLvMlksliPu7gcAAAAAFRFVcHr88cc1ZcoU1a9fv8Q3lw6/4zRz5szwuWAwqNzcXD322GP6+9//Lkl64IEH1LJlS/Xu3Tvc7q677tLJJ58cbvNnb7zxhtasWSOr1RpNqQAAAABQbaIKTg899JD69u2rH3/8Ubfeeqvcbrd+++03denSRS6XS99++60GDx6sQCCgl19+WUOGDAlvE/7SSy/pzTff1PLly2UwGJSdna2CggKZzeYSgWvHjh1q3ry5pEOPBb755pv6+OOPS2xnXi1yciSzWeL7TQAAAAAiiPoDuNOmTdP69eslSStWrNCAAQMkSUVFRbr++uvl8XgUFxen6dOna8aMGQoEAnryySd1zz33aNasWUpJSVFaWpq6du2qoqIiPfroo+HVpOzsbJ188snavHmzJOmZZ55RcnKyevbsqS1btmjnzp3avXu39u/fr2AwqN27d2vXrl3Kzs7W9u3bo7uRefOkV16R5syR1WYrsUsgAAAAAPxRVCtO+/bt08yZM/XTTz9Jkk4++WRt27ZNBQUFatGihbp06aLNmzerS5cuGjZsmD777DP169dPH374oT788EP17dtXDz/8sJYtW6b//ve/uvfee3XJJZfI4XAoFApp//79Gjx4sE466SRt27ZNkydP1rhx4+RyudSmTRtZrVbFxcWFV5/atm2rYDAor9erk046KRzoKiI9PV12u13SoY/wpqamRvNXAQAAAKAOMYRCoVA0HdasWRPeSjwUCunAgQNlbhXu8/kUHx8vg8GgYDBY4p2oisjOztaTTz6pBx54oFp3wfN4PHI4HHK73eHHCAEAAADUPdFkg6iD07GO4AQAAABAii4bRP2OEwAAAADUNQQnAAAAAIiA4AQAAAAAERCcAAAAACACghMAAAAAREBwAgAAAIAICE4AAAAAEAHBCQAAAAAiIDgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACEyxmOTRRx9Vt27ddNlll6mwsFBer1cWi0UGg6FUW5/PJ7PZLKvVGj733HPPaenSpZo1a5bee+89vfrqq/r222+rVFNGRobsdnuJc06nU6mpqVUaFwAAAMDxJybB6YILLtB1112ne++9V2azWWPGjJHVai0RnAKBgAoLC2WxWPTiiy/qpptuCl+Lj48PBymbzSaz2Vzlmnr06FHqnNVm04asLMITAAAAgBJiEpx69Oih7777Trt27VL37t11xx13lGqzbNkyPfDAA1q0aFFUY+fn58toNMpms0VXVL9+UrNm//vtcqlozhy5XC6CEwAAAIASavwdp++//17nn3++fD6funfvLkn6y1/+IqvVGj7Wr19fqp/X61UoFCp37PXr1+uMM87QP/7xj+gLS0qSkpP/dzid0Y8BAAAAoE6o8eDUqVMnJScnq3Pnzvroo4/C5zMyMlRUVCSn0ymTqfTC13nnnaekpCQ5nU7de++9Ja55PB7dc889uvDCC3X99ddr0qRJNX0bAAAAAOqwGn9Ur169epo1a5buvfdenXHGGZIko7FkXvvzb0n68ccfw3+eNm2ali1bFv6dmZmpSy+9VBs2bFCDBg3Knd/r9crr9YZ/ezyeytwGAAAAgDosJtuRGwwGPf300+F3h8raTS8a5557riZMmBAxNElSWlqaHA5H+EhJSanS3AAAAADqnph9x2n58uW67bbbVFxcrGAwWOJapHeZqmLs2LFyu93hIzs7u8bmAgAAAHB8ismuepL08MMPq7i4WCaTST6fT506dZLBYFCjRo1qdF6LxSKLxVKjcwAAAAA4vsVkxemjjz7SokWLNG3aNBkMBl155ZXatm2bioqKtHPnTp188sml+mzZskXvvfeehg8frscff/yIYweDQb3//vsqKiqqyVsAAAAAUIfV+IrT9u3bNWrUKN1+++069dRTJR167+iwUCik7777TosXL1Z8fLwk6YcfftAFF1ygXr16qU+fPmrUqJF27dol6dAK0qZNm+R2u+VwOPTf//5XN9xwg37//Xc1b9684oXl5Eh//JCuy1X1mwUAAABwXKrx4JScnKzHHntMV111VZnXDQaD3nzzTa1bt07jx4+XJJ1zzjnasWOHmjRpIkn6z3/+o99//13SoW3KmzVrpjZt2igUCik+Pl5PPPFEdKFJkubNK3XKarPJyfecAAAAAPyJIVSTOzMchTwejxwOh9LT02W320tcczqd4Z3/AAAAABzfDmcDt9utxMTEctvGbHOIo03nzp0j/uUAAAAAgBTD7cgBAAAA4FhFcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAgITgAAAAAQAcEJAAAAACIgOAEAAABABAQnAAAAAIjAVNsF1JaMjAzZ7fYS55xOp1JTU2upIgAAAABHq1oLTj///LMKCgp01lln1cr8PXr0KHXOarNpQ1YW4QkAAABACbUWnL766iu99tprWrdunYzGkk8MhkIheb1emc3mUtf+LBgMqri4WBaLJboC+vWTmjX732+XS0Vz5sjlchGcAAAAAJRQa8Fp2LBhWrBggXbv3q3k5GRJUiAQUHFxsfbs2aOePXvKarUqLi5OkpSVlaW2bdvq4MGDOnjwYLhPKBRS+/btNXv27OgKSEqS/n8MAAAAAChPzIJTs2bNtHfvXhkMhhLn/7i6EwwGFQqFVFhYqN9//71EO6fTqczMTM2dO1fz58/Xa6+9FpO6AQAAACBmu+rVq1dPn3/+ufx+/xGPYDAon88nq9VabfN6vV55PJ4SBwAAAABEI2YrTjabTcXFxRHbxcfHSzr0CJ7RaNRJJ50kScrNzVWbNm2Un5+vgoICLVq0SAUFBTr77LM1Z86cI46XlpamiRMnVs9NAAAAAKiTYvodp2AwWOG2BoNBFotFWVlZWrNmjVq2bKlNmzbppZde0qBBg7Rp0ya98cYb4aB1JGPHjpXb7Q4f2dnZVb0NAAAAAHVMzFacgsFgqfebIjGZDpW3YsUKtWvXrsw2kca0WCzR77gHAAAAAH8QsxWnnJwcJSQkVKrvM888o4EDB0qSjEajtm3bpiVLlmjr1q3VWSIAAAAAlCkmK06FhYVyuVxq0aJF1H1nzpypHTt26IYbbpAkdenSRQcOHNDNN9+s3r17V76onBzJbP7fb5er8mMBAAAAOK7FJDh9++23SkhIUOvWraPue/3112vAgAHhx/ZSUlK0YsUKSdKCBQs0ffr0yhU1b16pU1abTU6ns3LjAQAAADhu1XhwCoVCmjRpkq644oqIGzn8WSAQUJcuXY54PS8vT2eeeWal6kpPT5fdbi9xzul0lviuFAAAAABIMQhOS5Ys0bJly/T8889H3dfr9SojIyO82vRnCxYs0LRp0ypVV+fOnZWYmFipvgAAAADqFkMoFArV9CQ7d+5UcnJy1P0OHDigBg0aVGstHo9HDodDbreb4AQAAADUYdFkg5jsqleZ0CSp2kMTAAAAAFRGTD+ACwAAAADHIoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAgITgAAAAAQAcEJAAAAACIw1XYB0fjyyy+VlJSkbt26VXmsjIwM2e32Crd3Op1KTU2t8rwAAAAAjj0xCU6PPvqounXrpssuu0yFhYXyer2yWCwyGAyl2vp8PpnNZlmt1lLXXnjhBZ1//vnVEpx69OgRVXurzaYNWVmEJwAAAKAOiklwuuCCC3Tdddfp3nvvldls1pgxY2S1WksEp0AgoMLCQlksFt1333169tln5XA4ZLVaZTQeeqJw8+bNWrt2rWbMmBHu5/V6Va9ePWVkZERXVL9+UrNmFWvrcqlozhy5XC6CEwAAAFAHGUKhUCgWE23evFm7du1S9+7dy7y+bNkyPfDAA1q0aJGCwaCCwaBMpv/luqKiIjkcDu3YsUNOp7PSdXg8HjkcDmnoUKlVq4p12rlTeuUVrVq1Sl27dq303AAAAACOHoezgdvtVmJiYrlta3zF6fvvv9f999+vV155JRya/vKXv2jjxo3hNqtXry7Rx2g06qKLLpLL5Qqf8/l88vv96tmzZ6k5Jk+erMsuu6xmbgAAAABAnVfju+p16tRJycnJ6ty5sz766KPw+YyMDBUVFcnpdJZYWTps69atevLJJ5WZman//Oc/Sk9PVyAQUGZmpiZPnqyffvpJmZmZslqtCgaDR5zf6/XK4/GUOAAAAAAgGjUenOrVq6dZs2bpzjvv1BlnnHFoUmPJaf/8+3C/w7777jsNHz5ckvTrr79q4MCB+uWXX8LXLRbLEedPS0uTw+EIHykpKVW6HwAAAAB1T0y+42QwGPT000+HN1Yoaze9Pzu8CuV2u3XPPffonHPOUSgU0qRJk3TnnXfqlFNOUSAQiDjO2LFj5Xa7w0d2dnbVbgYAAABAnROzD+AuX75ct912m4qLi0s9Wlfe/hRTpkxR9+7d9e677yoxMVFffvmlPvvsM7Vr167Uu1FlsVgsSkxMLHEAAAAAQDRi9gHchx9+WMXFxTKZTPL5fOrUqZMMBoMaNWpUbr/Ro0frX//6l0wmk3r37q0hQ4ZoyJAhCgaD8vv9MaoeAAAAQF0Wk+D00UcfadGiRfr5559lMBh05ZVX6u6771bTpk3DbXJyckr0CQQC8nq96tSpk+Lj42UwGLRnzx6tWrVK48aNUzAY1KBBgypfVE6OZDZXrO0fdvcDAAAAUPfUeHDavn27Ro0apdtvv12nnnqqpEMbNhwWCoX03XffafHixYqPjw+fLygokMVi0c6dO8Pn/rjidFi3bt0qV9i8eVE1t9psVfp+FAAAAIBjV40Hp+TkZD322GO66qqryrxuMBj05ptvat26dRo/fnz4fEFBQam2RUVF8nq9kg5912nmzJnKz89XUlJS1HWlp6fLbrdXuL3T6QxvbgEAAACgbjGEytuZ4SiTlZWlpKQkNW7cWJJUXFxcYpWqIqL5OjAAAACA41c02SBmm0NUh7Zt25b4HW1oAgAAAIDKiNl25AAAAABwrCI4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAgITgAAAAAQAcEJAAAAACIgOAEAAABABAQnAAAAAIiA4AQAAAAAERCcAAAAACACghMAAAAARGCq7QIkyefzyefzyW63x2zOjIyMmM5XXZxOp1JTU2u7DAAAAKBOiWlwuvnmm9W4cWNNmjSpxPk33nhDH3zwgb799tsS5wOBgOLi4sK/r7zySl1++eW67bbbqlxLjx49qjxGbbDabNqQlUV4AgAAAGIopsEpISFBNput1HmLxVLm+UGDBmnBggVKSEiQJOXl5embb77R2LFjJR1aqRo5cmSpIFYh/fpJzZpF3682uVwqmjNHLpeL4AQAAADEUI0Hp+LiYoVCIZnNZhkMBhkMhlJt/ni+uLhYBoNBJpNJc+bMKdFu6NCh6t27t6655hpJ0oQJE1RYWFi5wpKSpOTkyvUFAAAAUKcYQqFQqCYnePXVV3XPPffIarUqPz9fBoMhvIJ0mNfrldfrVWJiorxerx544AE98cQTatCgQYmglZubK6vVGl6d8ng84VC2du1aNW/ePGI9Ho9HDodDGjpUatWqOm+15u3cKb3yilatWqWuXbvWdjUAAADAMe1wNnC73UpMTCy3bY2vON12223hd5LuvPNOOZ1OTZgwoUSb6dOn68MPP9T8+fPD5x566KFSYx1pxempp5464vyHQ9lhHo+nKrcDAAAAoA46qrcjv+yyy9SoUSM5nU45nU7NmjVLw4cPD/+eNGmSfD5fuWOkpaXJ4XCEj5SUlBhVDwAAAOB4EfPgNGnSpHDwOXzcddddZbZ97LHH9Pvvv8vlcsnlcumaa67RK6+8Ev6dk5Oj7t27lzvf2LFj5Xa7w0d2dnZN3BYAAACA41jMg9N9990XDj6Hj+eee67MtsOGDdNXX32ld955Rx9//LGkQ4/e3XrrrcrLy1N+fr6uvfbacuezWCxKTEwscQAAAABANI7aR/VCoZC2bt2qLl26aOrUqTIaD5VqsVjkcrk0adIkmUym8HkAAAAAqCkx/Y5TNNauXSuTySSfz6dt27apT58+4VWnRx55RD179tSIESMqP0FOjmQ2V1O1MeJy1XYFAAAAQJ0U0+AUCAQ0adIkPf/88yXOe71enXfeeSXOvfHGG7r44ouVk5Oj8ePHy2Qyye/3KxgMqlOnTnrttdckHVqZqpR58yrXr5ZZbTY5nc7aLgMAAACoU2IanEKhkMaNG6cHH3ywxPnp06dr+vTp4d/5+fn69ttvNWPGDHXt2lXnn3++JOnAgQPhD95effXV2r17t5o0aaJQKFTmh3XLk56eLrvdXrUbqgVOp1Opqam1XQYAAABQp9T4B3ArKxgM1sj7S9F85AoAAADA8SuabHDU7qzApg8AAAAAjhakEwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAhqLTgFg0EFg8Hamh4AAAAAKsxUWxPfdtttSkpK0qRJk0pdC4VC8nq9MpvNMhrLz3bBYFDFxcWyWCxRzZ+RkSG73R5Vn+OB0+lUampqbZcBAAAAHFNqLThZLBaZzeYyr23btk09e/aU1WpVXFycJCkrK0tt27bVwYMHdfDgQSUnJ0s6FLLat2+v2bNnRzV/jx49qnYDxyirzaYNWVmEJwAAACAKMQlOW7duldfr1SmnnBI+V9ZK0pYtW1SvXj21bNlSv//+e4lrTqdTmZmZmjt3rubPn6/XXnutakX16yc1a1a1MY41LpeK5syRy+UiOAEAAABRiElweuGFF7Rt2zbNmjWr3HajRo3ShRdeqPvuu6/mi0pKkv5/1QoAAAAAyhOT4GQymSr0DpLZbA4/vhcKhWQ0GnXSSSdJknJzc9WmTRvl5+eroKBAixYtUkFBgc4++2zNmTOnRusHAAAAULfFZFc9o9Eog8EQsd0f2xgMBlksFmVlZWnNmjVq2bKlNm3apJdeekmDBg3Spk2b9MYbbyg+Pr7cMb1erzweT4kDAAAAAKJxVH/HyWQ6tCC2YsUKtWvXrsw2kQJZWlqaHA5H+EhJSan2OgEAAAAc347q4HTYM888o4EDB0o6tHq1bds2LVmyRFu3bo3Yd+zYsXK73eEjOzu7pssFAAAAcJypte3IK2rmzJnasWOHbrjhBklSly5ddODAAd18883q3bt3xP4WiyXqbzwBAAAAwB/FZMXJ7/dXuG0oFCrx+/rrr9c333wTfmwvJSVFK1as0MaNG9WnT59qrRMAAAAAyhKTFafi4mLNmjVL8+fPD5/Lz8+XwWDQtGnTwucOHjyoc845J/w7EAioS5cuRxw3Ly9PZ555ZuWKysmRjvAB3uOWy1XbFQAAAADHpJgEp6efflpPP/10xHYDBgyQz+cL//Z6vcrIyAivNv3ZggULSgSvqMybV7l+xzirzSan01nbZQAAAADHFEPoz8/GHUUOHDigBg0aVOuYHo9HDodD6enpstvt1Tr2scDpdCo1NbW2ywAAAABq3eFs4Ha7lZiYWG7bo3pziOoOTX/UuXPniH85AAAAACAdI9uRAwAAAEBtIjgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEYKrtAmpLRkaG7HZ7bZdRq5xOp1JTU2u7DAAAAOCod1QHp9WrV+v000/XwYMHyww5ixcv1qBBg7R79+6ox+7Ro0d1lHhMs9ps2pCVRXgCAAAAIohJcLrxxhv16aefqkGDBpKk7du364QTTlBhYaG8Xq+cTqckyeVy6R//+IcmTpx4qDiTSWazORyaZs6cqWeeeUYrV66UJNlsNlmt1soV1a+f1KxZ1W7sWOZyqWjOHLlcLoITAAAAEEFMgpPZbNbdd9+tCRMmSJKSk5OVnp6uhQsXatmyZZo+fbokaejQoTKZ/ldSfHx8iWBks9lksViqp6ikJCk5uXrGAgAAAHBci8nmEH8OO3l5eXI4HGW2/WNwCgQCJX7HxcXVTIEAAAAAUI6YrDgZDIbwn/fu3SuDwRB+PK+8tj6fr0Rwqgyv1yuv1xv+7fF4qjQeAAAAgLon5tuRf/bZZ7rgggsq1Nbr9apevXpVmi8tLU0OhyN8pKSkVGk8AAAAAHVPTINTfn6+0tLSdP/990s6tLq0f/9+BYNB+Xy+Uu3379+vJk2aVGnOsWPHyu12h4/s7OwqjQcAAACg7olZcCosLNTAgQN16aWXqnv37pKks88+W8uXL5fVatWll15aqs/GjRt10kknVWlei8WixMTEEgcAAAAARCNmwclms2nkyJF65plnwuc6deqkPXv2yOfzadGiRaX6fPnll+ratWusSgQAAACAMsVkc4hAICBJ6t+/f8S2oVBIkvTrr7/qm2++0YsvvljqWrXIyZHM5uob71jjctV2BQAAAMAxIybBqbi4WE8++aSmTp1abruCggI9/PDDCgaDuuuuu3T11VfrxBNPDF/3er0l3oUKBoPy+/2VK2revMr1O45YbbYj7m4IAAAA4H9iFpweeOCB8Adwj2To0KHy+/3KyclRo0aN9OSTT5a43qtXL5122mnh34WFhSoqKqpUTenp6bLb7ZXqe7xwOp1KTU2t7TIAAACAo54hVK3Pv5XN7XYrLi7uqAgqHo9HDodDbrebjSIAAACAOiyabBCTFSeHwxGLaQAAAACgRsT8A7gAAAAAcKwhOAEAAABABAQnAAAAAIiA4AQAAAAAERCcAAAAACACghMAAAAAREBwAgAAAIAICE4AAAAAEAHBCQAAAAAiIDgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAggpgEp7179+raa6+Vz+eTJHm93kqN4/f7q7MsAAAAAKgQUywmadSokb7++mvNnTtXgwcP1l//+lctXbpUDoejRLuioiINGTJE06ZNKzVGbm6uOnXqpA8++EBnn312lWvKyMiQ3W6v8jjHOqfTqdTU1NouAwAAADiqGUKhUCgWE40YMUI5OTn68MMP5ff7ZTL9L7N5vV4NHTpU33//vebMmaPTTz+9zDFGjhypevXq6emnn650HR6Pp1Rgq8usNps2ZGURngAAAFDnHM4GbrdbiYmJ5baNWXD67bff1KBBAzVq1KjE+f3796t///4KBoP6+OOP1aRJExUVFclms8loNMpgMJQ7bigUUrdu3bR8+fIK1REOTv36Sc2aVfp+jgsulzRnjlatWqWuXbvWdjUAAABATEUTnGLyqJ4ktW7duszzzz77rPLy8rRs2TJZLBZJUnx8vCTpm2++Uc+ePcsd97XXXtNbb70VfUFJSVJycvT9AAAAANQ5Nb45xMKFC5WYmKhGjRrpggsuKHW9cePGat68eTg0SVIwGNRDDz2kVq1aRRy/bdu2uuSSS6qzZAAAAAAoocZXnC644AJt3bpVX331lZ5//vlS1/8YmA6Lj4/Xk08+qeeeey68+iQdehfKYDDIbDaHzxUVFemiiy464vxer7fELn4ej6eytwIAAACgjqrx4GSxWGSxWBQXF6e4uLgK9ytr6/GhQ4eqRYsWeuyxxyo8TlpamiZOnFjh9gAAAADwZ8f9B3DHjh0rt9sdPrKzs2u7JAAAAADHmJhtDvFn+fn5euKJJ7Rs2TIlJSXV2DyHV7wAAAAAoLJituK0ePFi7du3T0VFRZKkevXq6YcffpDFYtGdd94ZqzIAAAAAIGo1vuLk9Xo1cuRIrVq1Sueff766deum2267Teecc46mT5+uhIQEBYNBbd++XQUFBXK73WrRooWa/ekbS4FAQPn5+RG/61RhOTnSHzaZqJNcrtquAAAAADgm1HhwysrK0ooVK/Tll1+qRYsWWrNmjZ577jm99NJLys7OVkFBQak+O3fuLPF7/fr16tChg0wmk2bPnl09hc2bVz3jHOOsNpucTmdtlwEAAAAc1QyhUChU05MEg0EZjWU/FRgMBlVcXKxgMKhAIKBgMFjmV3s3b96slJSUEluRV8bhrwOnp6fLbrdXaazjgdPpVGpqam2XAQAAAMTc4WzgdrvLzCB/FJPNIY4Umg5fq8jmDSeddFJ1lqTOnTtH/MsBAAAAAKkObEcOAAAAAFVFcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAgITgAAAAAQAcEJAAAAACIgOAEAAABABAQnAAAAAIggZsHptdde05dffilJCgQC8vl8pdoEg0EVFRXFqiQAAAAAqBBTrCY68cQTddVVV+m///2vNm7cqHvvvVdxcXEl2gSDQZlMJv32228qKCiQxWIJt9mxY4eKioq0cuVKTZo0SXPmzJEk1atXT40bN466noyMDNnt9qrf2HHA6XQqNTW1tssAAAAAjlqGUCgUitVkt9xyiwYPHqzevXtHbHvqqafKZDJpw4YN2rJli959912tXr1aubm5ysjIUK9evbR161a1aNFC7733XoVr8Hg8cjgcVbmN447VZtOGrCzCEwAAAOqUw9nA7XYrMTGx3LY1vuIUCoXk9/sVHx+vadOmyWw2S5K2bt2qffv2lWrfoUMH2Ww2bdiwQV9//bWeffZZNWvWTHfffbfi4+O1fPlyPfDAA5o5c6ZmzZqlzz//XHl5edGvHvXrJzVrVh23eGxzuVQ0Z45cLhfBCQAAADiCGg9O27dv19lnn634+HgFg0Ht2bNH2dnZeuqpp/TDDz+oefPm4baLFi3SypUr1a5dO0nS66+/rlGjRmnOnDm6//77ZbVa5fV6tXv3bnXs2FFut1t5eXn68ccftX79+ugKS0qSkpOr81YBAAAAHKdqPDilpKRox44dkqS33npLy5YtU5MmTWQ0GjVgwAD99a9/DbfNzMyU0Xhov4qVK1fqxx9/1DvvvCOj0ag2bdpo586d2rhxo9544w1NmjRJ6enp2rFjh956662avg0AAAAAdVjMNofYvn270tLStHTpUknSFVdcobVr12rJkiXhNnfeeaeSkpIkSQ8++KD2798vu92u5557Tg0aNNDatWu1a9cuHTx4UD/++KPq1aun/v37lzuv1+uV1+sN//Z4PDVwdwAAAACOZzEJTrt27dKll16qQCCgm266SXfffbfGjBlTZtuZM2cqIyNDzzzzjNq3b68zzjhDHTt21Pz581VUVKTCwkIFAgHl5eUpGAxGnDstLU0TJ06s7lsCAAAAUIfUeHDKyspSnz59dNVVV2nlypVq1qyZZs2apRYtWmjBggXKzMzUgAEDtGnTpvAGBYFAQP3799err76qTZs2qVOnTuHNH37++Wdt2LBBN910k4LBYMQd8saOHVsipHk8HqWkpNToPQMAAAA4vtR4cDrxxBM1c+ZMGQwGrVy5Ui+//LK+/vprXXXVVerYsaO8Xq+ys7PVsWNHBQIBGY1GxcXFacqUKerfv78uvfRS/fDDD7r66qtVv359+Xw+5ebm6rLLLlMwGFReXp7ef/999erVq8z5LRaLLBZLTd8mAAAAgOOYsaYnsFgsOuecc8K/DQaDDAaDzjvvPGVmZurjjz9WSkqKMjMztXjx4nC7Cy+8UPHx8crOzlaHDh20f/9+bd26VR9++KHOPPNMbdmyRdu2bdP+/fuPGJoAAAAAoDrEbHOIYDAYfiepvG/uHv7u080336xBgwapS5cuOu2003TNNdfo0ksvlclk0sUXXyyv16tQKKSdO3dqx44d4S3PKywnR/r/b0rVaS5XbVcAAAAAHPViFpwKCwvl8/kkST6fT0uXLlXHjh0lSVarNfyoXnFxsebNm6cdO3Zo5syZstls6tOnj6ZPn66pU6dq48aN2rdvnx555JFwEDvttNP0008/RVfQvHnVen/HMqvNJqfTWdtlAAAAAEctQ6i85Z9q5PV65Xa71aRJkwq19/l8MkdYEQoEApKkuLi4Ctfh8XjkcDiUnp4e3nCirnM6nUpNTa3tMgAAAICYOpwN3G63EhMTy20bsxUni8VS4dAkKWJokqILTH/WuXPniH85AAAAACDFYHMIAAAAADjWEZwAAAAAIAKCEwAAAABEQHACAAAAgAgITgAAAAAQAcEJAAAAACIgOAEAAABABAQnAAAAAIiA4AQAAAAAERCcAAAAACACghMAAAAAREBwAgAAAIAICE4AAAAAEAHBCQAAAAAiMNV2AbUlIyNDdru9tss4ZjmdTqWmptZ2GQAAAEBM1Nng1KNHj9ou4Zhmtdm0ISuL8AQAAIA6ISbBafny5ZowYYJeeOEFnXjiiSooKJDZbFZ8fHy4jc/nU35+vqxWq2w2W/j8V199pVGjRmnz5s3hc2PGjFEgENAzzzxT+aL69ZOaNat8/7rM5VLRnDlyuVwEJwAAANQJMQlObdu2VZMmTdSxY0d98sknuvTSS4/Y9tVXX1UgEND9998vk8mk4uJi5efny+l0htsUFBRIkt555x1JktFo1N69e6MrKilJSk6O/mYAAAAA1DkxCU4Oh0MzZszQ3/72N1188cXav3+/LBZLeNUpKytLrVq1ktfrlcVikdFo1K233iqTyaSvvvpKY8aM0Y8//qj4+HjFxcXpgQceUFFRkaZOnapQKKTi4uJY3AYAAACAOqrGd9Xz+Xzas2ePJOnyyy+X0WhUw4YNlZCQIJPpUG6Li4uTxWJRYmKiLBaL4uPjw9eSk5N13XXX6cMPP9Qpp5wiSerZs2f4HSWDwSCz2VzTtwEAAACgDqvx4PTRRx+pdevWGjt2rHJzcyvcz+v1yu/3q2PHjnrwwQclSaeeeqokqXfv3rryyisrPI7H4ylxAAAAAEA0avxRvWuuuUZxcXG6++67ddlll+mCCy6oUL9+/fpp5cqViouLkyQVFRXJ7/eH33U6cOCA5s+fr969e5c7TlpamiZOnFi1mwAAAABQp9X4ipPBYNDgwYO1YcOGCocm6dBuetnZ2dqzZ49cLpduv/12PfDAA3K5XHK5XEpJSZHD4Qg/BngkY8eOldvtDh/Z2dlVvSUAAAAAdUyNB6fDrFarbrnlFm3durXCfUaMGKEbbrhBkrRw4UKdffbZ4Wsul0tz585Vjx49dODAgSOOcfjdqT8eAAAAABCNmAWnefPm6eOPP1ZCQkKF2r/99ttasWKFnnvuOS1btkxbtmzRRRddJOnQhhN5eXkaM2aM2rZtq7/97W8KhUI1WT4AAACAOiwm25FL0tSpU3XHHXeocePGEdu6XC5NmDBBn376qWw2m26//Xbde++94d3zcnJyJEmNGjXS22+/rS+//FIGgyG6gnJyJHbjqxyXq7YrAAAAAGIqJsFp8eLFWrp0qd555x3t2LFDksKbPkiHgpLdbpff71dxcbFSU1P166+/Kjc3V3379pXD4dD9998fbr9582bVr19f8fHxio+P18CBA6Mvat68Kt9XXWa12Up8lBgAAAA4ntV4cCouLtY999yjSy+9VCkpKerQoYO2bNkik8kkg8Egh8Oh3r17KxQKyefzyefzaceOHdqwYYMGDx6sc889V++8845MJpP8fr/69OmjFStWqG/fvlWqKz09XXa7vZrusu5xOp1KTU2t7TIAAACAmDCEavjloIKCAj333HNq166drrjiiqj6Ll68WOeff36Jc6tWrVJCQoLatWtXqXo8Ho8cDofcbjcbRQAAAAB1WDTZoMaD09GG4AQAAABAii4bxGxXPQAAAAA4VhGcAAAAACACghMAAAAAREBwAgAAAIAICE4AAAAAEAHBCQAAAAAiIDgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABCBqbYLqC0ZGRmy2+21XcZxyel0KjU1tbbLAAAAAKpNzILTkiVL1LFjRzVo0ECSlJaWJqPRqPvvv1+7du1SZmamtmzZomXLlik/P1+zZs3Shx9+qI8//lj/+c9/lJSUpKKiIlksFplM/yvb6/UqPz9fdrtdZrO5wvX06NGjum8R/89qs2lDVhbhCQAAAMeNmAWn+fPna8iQIXr33Xd17rnnKi8vTwaDQZL073//W99++6369++vHj16KDk5WaFQSOedd55ef/11tW/fXm+88Yb69+9/xPEXLlyoSy65pOIF9esnNWtW1dvCn7lcKpozRy6Xi+AEAACA40bMgtOTTz6pTp06afbs2Tr33HPDoUmS4uLi1KtXL40fP75En2bNmumzzz7TV199pUsuuUS5ubmyWCzauXOn2rRpo6KiIoVCIXm9XiUkJERXUFKSlJxcHbcGAAAA4DgX03ecrr32Wl100UVq3LixJCkUCumDDz7Q4MGD5ff7w+18Pp8KCwtlMBiUmJio3r17S1L4Mb+4uLjwP00mk6xWayxvAwAAAEAdE5Nd9fbs2aMzzjhDc+fOVdOmTbVv3z6NGDFCI0eO1K+//qq4uDi9/PLLat26tQwGg0455RRNnTpVJ554oiZNmqSCgoJKz+31euXxeEocAAAAABCNmASnJk2a6J///KdGjx6tZ599Nnze5XLpvffek9/v1+23364VK1ZIkrZs2aKHHnpIjz/+uCZPnqxdu3ZVeu60tDQ5HI7wkZKSUuX7AQAAAFC3xCQ4GQwGDR48WGvXrlX37t319NNPa+7cuZo7d64yMjJUXFys+Pj4En1MJpNGjhyp3377TSeddFKl5x47dqzcbnf4yM7OrurtAAAAAKhjYvoB3F9++UWrV6/WypUrddJJJ2nYsGF66qmntHPnTtWvX7/MPoWFhRoxYoRycnIqNafFYlFiYmKJAwAAAACiEdPgdMMNN8hgMGjWrFn6y1/+Ej6/ceNGtWzZssw+M2fO1Ndff6169erFqkwAAAAAKCFmu+r98MMP2rdvn6699toS53fv3q1Vq1bpvPPOUygUKnEtEAjoueee00MPPVT9O+fl5EhRfDAXFeRy1XYFAAAAQLWLWXB69tlnddVVVykhIUHLly/XunXr1KVLF02ZMkXdu3dXs2bNwptABINBGY1GvfPOO8rNzdV1112nrVu3ymQyKS4uTvv27ZN0aLc+o9Eov9+vQCCgVq1aVbygefNq4C4hSVabTU6ns7bLAAAAAKpNTIKT2+3WypUrNXnyZEnSyy+/LLPZrGuvvVb33HOPHnroIUmHtg6XpKKiIvn9fo0bN07XXnutEhIS1KFDB4VCIZlMJhkMBjkcjvA5r9eruLg45efnV7im9PR02e326r9ZyOl0KjU1tbbLAAAAAKqNIfTn5+NqSHFxsYLBoCwWS4Xa79+/X1OmTNGAAQN05plnVlsdHo9HDodDbrebjSIAAACAOiyabBCz4HS0IDgBAAAAkKLLBjHdVQ8AAAAAjkUEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAhMtTm53++XyRRdCUuWLFHnzp1lt9urNHdGRkaVx0D1cDqdSk1Nre0yAAAAgCMyhEKhUG1M/PPPP+u0005Tfn6+EhISymxz0003qVGjRnrkkUdUv359+f1+paSk6L333lPPnj0rNa/H45HD4ahC5ahuVptNG7KyCE8AAACIqcPZwO12KzExsdy2tbbiZLFYJElWq/WIbaZMmaK77rpLHTp00KpVq/T1118rJydHQ4cOLdHO5/OpZ8+eevfddyteQL9+UrNmlSkd1cnlUtGcOXK5XAQnAAAAHLVqLTgZjcYS/yxL48aNNWvWLC1dulT169fXww8/rP/+978677zzql5AUpKUnFz1cQAAAAAc92r1Hac/CwaD8vl8slgs2r59uxYvXqzrrrtO5513nm644QYlJibqpptuKtWvV69eeumll2qhYgAAAAB1Qa3vqmcwGMJHXFycbDabNmzYIJfLpUceeUSXX365duzYoauvvlr33Xef+vXrp5UrV6pPnz7atGmTZs6cqdzc3COO7/V65fF4ShwAAAAAEI1aD0779u0LH7t27dKWLVt00kknqUuXLlq9erWaN2+utWvXasCAAfL5fKpfv74aNmyo7du3a8uWLfJ6veW+J5WWliaHwxE+UlJSYnh3AAAAAI4Htbar3qZNm3TyyScr0vSHd927+eab9dlnnykYDKphw4bh64WFhTp48KDatGmj1atXl+rv9Xrl9XrDvz0ez6HwNHSo1KpVdd0OKmvnTumVV7Rq1Sp17dq1tqsBAABAHXJM7KpXUc8884wCgYCmTZump556Si1bttSFF16o1atX68orr9Ts2bO1Zs0aPfzww2X2t1gs4R38AAAAAKAyav1RvfKEQiG9/vrr6tGjh6xWqw4cOKD69etLksaMGSOv16vCwkLVq1dPNputlqsFAAAAcLw6qlecvvjiC5nNZl1wwQWSpNzcXNWvX18tW7bUXXfdpby8PBUWFspsNqu4uFjx8fEVHzwnRzKba6hyVJjLVdsVAAAAABHFJDgVFRVpz549MpvNMhgMkiTX//8H8+7du0u1Ly4uVtOmTZWWlqaRI0eGz9tsNt1+++3h3y+++KJycnJkMpmUkJCgO+64o+JFzZtXybtBdbPabHI6nbVdBgAAAHBEMdkcYunSpbrwwgtltVrL/eCtdOhbToWFhfr66681cOBA/f7777Lb7WW2XbhwoR588EGlpaXpkksuqVAth18AS09PP+K4iC2n06nU1NTaLgMAAAB1zFG3OcR5550nn88Xdb9169aVG25+/vln9erVSxdeeGHUY3fu3DniXw4AAAAASLW4HXltiSZVAgAAADh+RZMNjupd9QAAAADgaEBwAgAAAIAICE4AAAAAEAHBCQAAAAAiIDgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiMBU2wVE68svv1RSUpK6detWpXEyMjJkt9urqSrUFqfTqdTU1NouAwAAAMe5mAYnn8+noqIimc3mMq8XFxfLaDSqXr16RxzjhRde0Pnnn1/l4NSjR48q9cfRwWqzaUNWFuEJAAAANSqmwemLL77QNddcI4vFUuZ1n8+nu+++W0888YQWL16sAQMGyOFwyGq1ymg89FTh5s2btXbtWs2YMSPcz+v1ql69esrIyKh4Mf36Sc2aVeV2UNtcLhXNmSOXy0VwAgAAQI2KaXDq37+/CgsLK9T2vPPO0549e2Qy/a/EoqIiORwO/fjjj3I6nVUrJilJSk6u2hgAAAAA6oSYBadgMBheNapo24suukgulyt83ufzye/3q2fPnqX6TJ48WZdddll1lQsAAAAAYTEJTnl5eWrUqJHsdns4PAUCAcXFxZVqGwgEdPDgQfn9fm3dulXPPfec+vbtq4ULF6pjx45q9v+P133xxRe69NJLFRcXp27duikYDJY5t9frldfrDf/2eDw1cIcAAAAAjmcx2Y7cbrfL5/Np//79crlccrlc6t+/v26++ebw78NHbm6u/H6/JJXYJOK7777T8OHDJUm//vqrBg4cqF9++SV8/UjvTaWlpcnhcISPlJSUGrxTAAAAAMejWvmO02+//ab3339fl19+efhcWe8+HX6/ye1265577tE555yjUCikSZMm6c4779Qpp5yiQCBQ7lxjx46V2+0OH9nZ2dV7MwAAAACOe7USnEaPHq077rhDF154oSTpxRdfVP/+/cMrTX82ZcoUde/eXe+++64SExP15Zdf6rPPPlO7du20evXqcueyWCxKTEwscQAAAABANGIenN58803t2rVLTzzxRPjcrbfeqry8PI0ePbrMPqNHj9batWuVmZmp8847T2lpacrMzNTGjRvVqVOnWJUOAAAAoI6K6XbkS5Ys0cSJE5Weni6TyaT9+/eHj2HDhum2225Tp06dNGLECEmHNorwer3q1KmT4uPjZTAYtGfPHq1atUrjxo1TMBjUoEGDKldMTo50hA/x4hjxhx0XAQAAgJoU0+B0yy23KDc3V6effroCgYCaNm2qJk2aqGnTpmrcuLFGjx6tu+++W+eff77at2+vgoICWSwW7dy5MzxG7969NWTIEA0ZMiR8rlu3btEXM29eddwSapnVZqv6N70AAACACGIanGbPni2j0ahWrVqpfv36Zbbp27ev2rZtK0kqKCgodb2oqCi8vbjP59PMmTOVn5+vpKSkqGpJT0+X3W6P8g5wtHE6nUpNTa3tMgAAAHCcM4RCoVBtFxGNrKwsJSUlqXHjxpKk4uJixcfHV7i/x+ORw+GQ2+1mowgAAACgDosmG8R0xak6HF6NOiya0AQAAAAAlVEr25EDAAAAwLGE4AQAAAAAERCcAAAAACACghMAAAAAREBwAgAAAIAICE4AAAAAEAHBCQAAAAAiIDgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACEy1XUBtycjIkN1ur+0yEANOp1Opqam1XQYAAACOYTEJTsuXL9eECRP0wgsv6MQTT1RBQYHMZrPi4+PDbXw+n/Lz82W1WmW1WuX1emU2m2U0lr8oFgwGVVxcLIvFElVNPXr0qNS94Nhjtdm0ISuL8AQAAIBKi0lwatu2rZo0aaKOHTvqk08+0aWXXnrEtq+++qp69eqlnj17ymq1Ki4uTpKUlZWltm3b6uDBgzp48KCSk5MlSaFQSO3bt9fs2bOjK6pfP6lZs0rfE44RLpeK5syRy+UiOAEAAKDSYhKcHA6HZsyYob/97W+6+OKLtX//flkslvCqU1ZWllq1aiWv1yuLxSKLxaLff/+9xBhOp1OZmZmaO3eu5s+fr9dee61qRSUlSf8fvgAAAACgPDUenHw+n3Jzc9W0aVNdfvnlkqSGDRuWaBMXFxcOTAAAAABwtKnxXfU++ugjtW7dWmPHjlVubm6F+4VCIRkMBrVp00Zt2rRRbm6u2rRpo1GjRmn27Nlq06aNkpOTddVVV9Vg9QAAAAAQg+B0zTXX6M0339SMGTP0888/V7ifwWCQxWJRVlaW1qxZo5YtW2rTpk166aWXNGjQIG3atElvvPFGiQ0myuL1euXxeEocAAAAABCNGg9OBoNBgwcP1oYNG3TBBRdE1ddkOvQk4YoVK9SuXbsjjl+etLQ0ORyO8JGSkhJVDQAAAAAQsw/gWq1W3XLLLdq6dWvUfZ955hkNHDhQkmQ0GrVt2zYtWbKkQmONHTtWbrc7fGRnZ0c9PwAAAIC6LWbBad68efr444+VkJAQVb+ZM2dqx44duuGGGyRJXbp00YEDB3TzzTcrMzMzYn+LxaLExMQSBwAAAABEI2bBaerUqbrjjjvUuHHjqPpdf/31+uabb8KP7aWkpGjFihXauHGj+vTpUxOlAgAAAEAJMfmO0+LFi7V06VK988472rFjhySFP2wrSS6XS3a7XX6/X8XFxUpNTVVcXJwCgYC6dOlyxHHz8vJ05plnVq6onBzJbK5cXxw7XK7argAAAADHAUMoFArV5ATFxcU655xz1LhxY33xxRfq0KGDtmzZIpPJVGJjh1AoJJ/PJ5/Ppx07duiEE06Q0WiUz+cLrzb92YIFCzRt2jTNnTu3wvV4PB45HI6q3haOIVabTRuyspSamlrbpQAAAOAocjgbuN3uiK/01PiKU3FxsQYNGhTeFW/dunUV7rt///4jhiZJ6t27t3r37l2putLT02W32yvVF8cWp9NJaAIAAECV1PiK09EmmlQJAAAA4PgVTTaI2eYQAAAAAHCsIjgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEYIrVRF6vVyaTSXFxcWVe9/v9CgaDMpvNMaknIyNDdrs9JnPh6OV0OpWamlrbZQAAAOAoF7PgdNZZZ+mnn34qt83VV1+tDz/8UJK0du1a2Ww2nXzyySXaPPLII/J6vXr88cerVE+PHj2q1B/HB6vNpg1ZWYQnAAAAlCtmwSk9PV3x8fEyGg89HXjPPfcoKSlJ48aNk3RoRcpgMITb//LLL/r73/+uCRMm6KSTTtKoUaNks9nkcrkUCoX02Wefye/3q127dpo9e3b0BfXrJzVrVi33hmOUy6WiOXPkcrkITgAAAChXzIKTw+Eo8TsuLk4mk0lWq1WSwv887G9/+5vatGmjpUuXqlevXtq0aZMk6cknn1RRUZEmTJigRYsW6dFHH61cQUlJUnJy5foCAAAAqFNiEpyKiopkNpvDq01H4vf7VVxcrLi4OH3yyScaOHCgjEaj2rRpE+7vcrkUDAb14YcfKj8/X/v27dMpp5yizMzMmL0fBQAAAKBuicmuen379lVcXJwMBkP4eOGFFzRx4sQS5+Lj43XOOedo69atevDBB3X++efLZrMpMzNTP//8szIzM3X33Xfr9ttvV2Zmpt58802dffbZ+vnnn48YmrxerzweT4kDAAAAAKIRkxWnuXPnymg0hlecgsGgWrduLavVqnXr1oV32guFQvL7/apfv77Wrl2rKVOmqFmzZlq6dKlGjBghs9ms/fv3l1hxSklJkcViOeLcaWlpmjhxYixuEwAAAMBxKibB6c/bfi9YsEANGjSQ3W7XnDlzdMMNN5TqY7PZdOmll6qwsFA9e/bU+vXrZTKZNGnSpPA7ThUxduxYjRkzJvzb4/EoJSWlSvcDAAAAoG6J2eYQh/n9fo0bN0633367Tj75ZN1888265JJL1KyMHe7uu+8+9e3bV1u3btV3330ns9msvXv3KhgMasGCBQoGg/J4PPr888/VunXrMuezWCzlrkgBAAAAQCQxDU6hUEh33HGHvF6vhg0bpoSEBF100UUaOHCgPv/88xI776Wnp2vDhg1asGCBbDZb+Pwfd9UDAAAAgFiIWXDauXOnhg0bplWrVmnJkiVKSEiQJL366qvq3bu3unbtqrffflvnnnuuQqGQHnjgAf3jH//QypUrdeONNyoxMVFGozG84jR37lxJUkFBgc4++2y99dZb0RWUkyOxC1/d5nLVdgUAAAA4RtR4cNq1a5eee+45vfTSS2rfvr1Wr16t5s2bh6/Xq1dP33zzje677z51795dnTt31u23366srCyNHDlS9evX15YtW8LtH330UXm9Xj322GNVK2zevKr1x3HBarPJ6XTWdhkAAAA4ytV4cEpISFBmZqaef/55XXvttWV+y8lsNmvq1KkaPny4/vvf/+q6665Tu3btVL9+/VJtvV6vioqKqlxXenp6qU0rUPc4nU6lpqbWdhkAAAA4yhlCoVCotouIJY/HI4fDIbfbrcTExNouBwAAAEAtiSYbxOQDuAAAAABwLCM4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAgITgAAAAAQAcEJAAAAACIgOAEAAABABAQnAAAAAIiA4AQAAAAAERCcAAAAACACghMAAAAAREBwAgAAAIAITLVdwN69e+V0OmU0xjbDZWRkyG63x3ROHHucTqdSU1NruwwAAADUspgEp/PPP1/bt2+XJO3cuVP79u1TYmKiJGngwIEaM2aMBgwYEG4fCoXk9XplNpsjBqpgMKji4mJZLJaoaurRo0d0N4E6yWqzaUNWFuEJAACgjotJcLLZbHrzzTe1fv16zZ07VzabTX6/X3FxcbJarapfv74kye/3S5J27Nihnj17ymq1Ki4uTpKUlZWltm3b6uDBgzp48KCSk5MlHQpZ7du31+zZs6Mrql8/qVmz6rtJHH9cLhXNmSOXy0VwAgAAqONiEpwOrxotXLhQ/fv315QpU/T222/LZDJpy5YtGjFihBISEuT3+zVy5EiNHj1av//+e4kxnE6nMjMzNXfuXM2fP1+vvfZa1YpKSpL+P3wBAAAAQHli9o5TYWGhFi5cqOeff15+v18tW7bUddddp969e+uBBx5Qly5dNGvWLA0fPjxWJQEAAABAhcQsOH3//fdKSkpScnKy3G63xowZo969e4ev//vf/9bWrVs1YsQISYcewTMajTrppJMkSbm5uWrTpo3y8/NVUFCgRYsWqaCgQGeffbbmzJlzxHm9Xq+8Xm/4t8fjqaE7BAAAAHC8itlWdhdeeKFOPvlkPfvss2rQoIFGjx6tbdu2STq0wcOePXs0efLkcHuDwSCLxaKsrCytWbNGLVu21KZNm/TSSy9p0KBB2rRpk9544w3Fx8eXO29aWpocDkf4SElJqdH7BAAAAHD8idmKk9Fo1HPPPadzzz1Xb7zxhnw+n2bNmqXNmzcrKytLiYmJ6tGjh+rXr6/ly5cfKs50qLwVK1aoXbt2ZY5rMBjKnXfs2LEaM2ZM+LfH4yE8AQAAAIhKTL/jlJKSIr/fr9mzZ+uUU07R6tWrddZZZ6lRo0ZavXr1Efs988wzGjhwoKRDAWzbtm1asmSJtm7dGnFOi8US9VblAAAAAPBHMXtUb+fOnUpJSVH37t3VunVrSdKjjz6qxx57TAUFBZo3b16Z/WbOnKkdO3bohhtukCR16dJFBw4c0M0336zMzMxYlQ8AAACgDovJilMgEFBycrJWrVoVDk2PPPKI1q9fr3fffVft27fXqFGj1LVrVzVv3rxE3+uvv14DBgwIP7aXkpKiFStWSJIWLFig6dOnV66onBzJbK70PaEOcLlquwIAAAAcJWISnIqLiyVJrVu31u7du/WPf/xDS5cuVXp6umw2m/r166clS5bo7LPP1owZM3TRRRdJOhS4unTpcsRx8/LydOaZZ1auqCOscAF/ZLXZ5HQ6a7sMAAAA1LKYBqfffvtNp59+uvr06aOMjAw1aNAg3Oapp55SYmKiRowYoeXLl6tRo0byer3KyMgIrzb92YIFCzRt2rRK1ZSeni673V6pvqg7nE6nUlNTa7sMAAAA1DJDKBQKxXLC3NxcNWzY8IjXvV5veDOHAwcOlAhX1cHj8cjhcMjtdisxMbFaxwYAAABw7IgmG8Rsc4jDygtNkkrsgFfdoQkAAAAAKiPmwQkAAAAAjjUEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAhMVR3A6/XKbDbLYDBURz0xk5GRIbvdXttlANXK6XQqNTW1tssAAAA47lQ5ON1www068cQT9dRTT5W6lp+fr40bN8pqtUqSnnzySTmdTg0bNkxer1edO3fW8uXLlZiYqFAopMLCQnXt2vWIc5166qkaMmSIHn744aqWrR49elR5DOBoY7XZtCEri/AEAABQzaocnOLj44/4H2l79uzR5MmTZbPZJEmrVq2S3W7XgQMHwm1eeuklxcXFKRgMqqCgQLNmzTriXA0bNlTjxo2rWvIh/fpJzZpVz1jA0cDlUtGcOXK5XAQnAACAahZVcBozZoxefPHFEo+4HTx4UPPmzdO//vWv8Dm3261HHnlEY8eO1c6dO7Vv3z5J0oYNG5SQkKCDBw+G2/7www8ymQ6V0bt3b0lSTk6O4uLiVL9+fcXFxf2vWJMpvHp1mM/nk9vtVsOGDcPjVEhSkpScXPH2AAAAAOqsqIKT2WzWNddco+nTp4fPnXvuuRo3bpwuv/zy8LmePXuGw9XGjRs1d+5c/fDDD0pMTCwRhA4/ntezZ099+umnWrNmjSTp+uuv15dffimj0Vji3alAIKAffvhBw4YNC58LBoMKhULauHGj2rRpE93dAwAAAEAFRBWc/ryiU1xcrF9++UUNGjQo1fbw43kffPCBTj31VH3zzTcqLi6W0VhyIz+/369gMKibbrpJAwcOlCTNmTNHVqu1VNvu3btr2LBhGjp0aInzhYWFslgsZdbs9Xrl9XrDvz0eT4XuFQAAAAAOq9I7Tu+++64CgYCGDBmi9957T2eddVapNsuXL9eNN94oi8VyxJ33Xn/9dXm9Xj399NNq1aqVEhISoqrjcEgrS1pamiZOnBjVeAAAAADwR5X+jtOvv/6qMWPGaPr06Xr22Wd1+eWX65lnninV7rbbbtOPP/6ozz77TLfccovi4+OVnZ2tOXPmKDMzUwsWLNAFF1ygRYsWhd9xOqxDhw6yWq3h4/vvv9dtt91W4tyyZcvKrXPs2LFyu93hIzs7u7K3DAAAAKCOqlRwWrZsmS688EINGTJEV111lfr27auFCxfq0Ucf1UsvvVSi7QcffKAuXbrommuukSQNHTpUTqdTffr00UMPPaROnTrphBNOkM1mK7VyZDAYNG3aNBUVFamoqEjBYFDFxcXh34e/IVUei8WixMTEEgcAAAAARKNSwWnbtm0aOHCgpk6dGj7XtWtXff/99yU2bpCk6667TkuWLNGYMWM0e/ZsZWVl6eWXX9b27du1ZcsWde3aVb179y5zm/E/v+NU5g1UoA0AAAAAVEWl3nEaPHiwBg8eXOr8KaecUurcfffdp3feeUf9+/fXyy+/rPnz5+vuu+/WP//5T+3cuVOTJk3SgAED9Prrr+uSSy4p0fdI70T9USgUqswtSDk5UoTVKuCY4nLVdgUAAADHraiCUzAYLPf6wYMH5Xa7tXfv3vBK0B133KFLLrlE6enp6tOnjwYMGKAvvvhCL7zwgkKhkLp06aIXX3xRgwYN0vnnn69PP/20xHy33XabRo4cWemajmjevMr1A45iVptNTqeztssAAAA47kQVnNxud7nXZ82apZEjR6pTp07q0aOHgsGgrrvuOgUCAV177bVauXKlTjjhBHXp0kVut1tvv/22JKlPnz7atGmTduzYUWK8oqIivfrqq6W2Hz/MYDCoqKgomlsIS09PL/EhX+B44HQ6lZqaWttlAAAAHHcMoUo/61ZaMBiUz+eT1WoNn/N6vaW+sVRYWFjuFuI1yePxyOFwyO12s1EEAAAAUIdFkw2qdWcFo9FYIjRJKvPDtLUVmgAAAACgMtiSDgAAAAAiIDgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAJTbRdQWzIyMmS322u7DACSnE6nUlNTa7sMAACAI6q14BQMBiVJRmPFFr3cbre2bt2q0047rVrm79GjR7WMA6DqrDabNmRlEZ4AAMBRq9aC02233aakpCRNmjSpQu1nzJihqVOnav369bJarVUvoF8/qVmzqo8DoGpcLhXNmSOXy0VwAgAAR61aC04Wi0Vms7lCbb1eryZPnqwXXnihekKTJCUlScnJ1TMWAAAAgONaTDaH2Lp1q3799deSE5fxiN6WLVu0b98+SdLw4cPVoEEDNWjQQE6nU9u3b9eNN94YPtegQQPVr19fVqtVW7dujcVtAAAAAKijYhKcXnjhBY0fPz5iu1GjRunNN9+UJD3//PPKzc3Vxo0bZbFYtGDBAuXk5GjHjh06cOCADhw4II/Ho/379/N4DwAAAIAaFZPgZDKZZLFYIrYzm83hx/fMZrMMBoNGjBihK6+8Un/961/1xRdfqHXr1uH2BoNBCQkJMhgMRxzT6/XK4/GUOAAAAAAgGjEJTkajsdxwc9gf2/h8Pt1zzz1au3at/vOf/0iSrFarTKb/vZYVCoXk9XrLHTMtLU0OhyN8pKSkVPIuAAAAANRVR+13nPLz8/Xaa6+pfv366ty5sySpsLBQe/bsUZs2bSQdCldGo1Fbtmw54jhjx47VmDFjwr89Hg/hCQAAAEBUjtrg1LBhQ7ndboVCIcXFxUmSFi1apCFDhmjTpk0VHsdisVToMUEAAAAAOJKYPKpXWUajUePHj9ewYcNKXRs/frweeuihWqgKAAAAQF0TkxUnv99f4bahUCj85/fee09Tp07V3LlzS7W78sor1atXLx08eFDPPvts9EXl5EgV/I4UgBrkctV2BQAAABHFJDgVFxdr1qxZmj9/fvhcfn6+DAaDpk2bFj538OBBnXPOOZKkd955RyNHjtTHH3+sXr16SZIOHDgQ3kCiS5cu+vrrr9WzZ0/ZbDY99dRT0RU1b14V7wpAdbHabHI6nbVdBgAAwBHFJDg9/fTTevrppyO2GzBggHw+nyRp1apVmjVrli699FJJ0uWXX66FCxfq2muvDbfv3Lmz5s2bpwYNGkRdU3p6uux2e9T9AFQ/p9PJ99gAAMBRzRD647NxR7H169fLaDSqbdu2VRrH4/HI4XDI7XYrMTGxmqoDAAAAcKyJJhsctbvq/Vn79u1ruwQAAAAAddRRvaseAAAAABwNCE4AAAAAEAHBCQAAAAAiIDgBAAAAQAQEJwAAAACIgOAEAAAAABEQnAAAAAAgAoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiMBU2wXUloyMDNnt9touA0ANcDqdSk1Nre0yAADAceSYCk5ffvmlkpKS1K1btyqP1aNHj2qoCMDRyGqzaUNWFuEJAABUm5gEp7vuukuNGzfWfffdJ6vVqrS0NO3evVvPPPOMJCk+Pl7NmzdXIBCQ1+vV3r17yxznhRde0Pnnn18twUn9+knNmlV9HABHF5dLRXPmyOVyEZwAAEC1iUlwSktL05VXXqlt27bptddek81mk81mC1+vV6+etmzZot27d+vMM8/U4sWLNWDAADkcDlmtVhmNh17F2rx5s9auXasZM2aE+3q9XtWrV08ZGRnRFZWUJCUnV8ftAQAAADjOxSQ42e12ffrpp/r111/10ksvadmyZXK73XrppZfUu3dvxcXFhdsajUadd9552rNnj0ym/5VXVFQkh8OhH3/8UU6nMxZlAwAAAICkGASn4uJi7du3T8nJySoqKtKHH36oHTt2yOv1qqCgQKeddpqCwWCJPkajURdddJFcLlf4nM/nk9/vV8+ePUvNMXnyZF122WVlzu/1euX1esO/PR5P9dwYAAAAgDqjxoPTkiVLNGjQIKWlpWnYsGH65ptvNHXqVO3evVtPPvmkpEOrSR07dpTf7w/327p1q5577jn17dtXCxcuVMeOHdXs/99J+uKLL3TppZcqLi5O3bp1KxW8/igtLU0TJ06s2ZsEAAAAcFyr8e84XXjhhfr000/1xBNPaOXKlWW2MZlMyszM1KJFi8Ln6tWrF/7zd999p+HDh0uSfv31Vw0cOFC//PJL+LrFYjni/GPHjpXb7Q4f2dnZVbwjAAAAAHVNTN5xOvfcc5WVlSWLxaI2bdrIaDTKYDDo3Xff1caNGxUfH1+6sP9/v8ntduuee+7RtGnTFAqFNGnSJN1555065ZRTFAgEIs5tsVjKDVYAAAAAEEnMvuP03HPP6YwzzpDZbNacOXNktVp1ySWXyOVyKRQK6e6771ZBQUGpflOmTNFHH30ko9GotLQ0NWjQQA6HQx9++KFmzZoVq/IBAAAA1GExCU5+v1/PPvusFi1aJKPRqCuuuEIGg0HBYFC//vqrzjzzzPB7T1999VWJvqNHj9a//vUvmUwm9e7dW0OGDNGQIUMUDAZLvBMVtZwcyWyu4p0BOOr8YVMZAACA6hKT4PTRRx+pa9euat26tUKhkD799FNZrVadf/75WrBggc4777xSfQ5/DLdTp06Kj4+XwWDQnj17tGrVKo0bN07BYFCDBg2qfFHz5lXhjgAczaw2G58tAAAA1arGg1MoFFJaWpoef/xxSQqvEjVu3FgTJkzQ2LFj9eOPP0o6tHX5YQUFBbJYLNq5c2f43B9XnA7r1q1bpepKT0+X3W6vVF8ARzen06nU1NTaLgMAABxHajw4vffee8rNzVXv3r0lSfPmzVOrVq0UCAQ0a9Ys9e/fX6mpqVq0aJFefvllJSUlSVKZ7zsVFRWFv8nk8/k0c+ZM5efnh/tEo3PnzkpMTKzCnQEAAACoKwyhUChUkxMUFhYqMzNTZ5xxRonza9as0eOPP67p06fLbrdrw4YN+uCDDzRkyBCdeOKJZY6VlZWlpKQkNW7cWNKhFaqyduQrj8fjkcPhkNvtJjgBAAAAdVg02aDGg9PRhuAEAAAAQIouG9T4B3ABAAAA4FhHcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAgITgAAAAAQAcEJAAAAACIgOAEAAABABKZYTvbJJ5/o+++/11NPPRU+FwqFtHfvXm3ZskXff/+9iouLdd9996moqEgWi0UGg0GSVFhYKLPZrLi4uBJj+v1+hUIhxcfHR1VLRkaG7HZ71W8KAMrhdDqVmppa22UAAIAqiklwOhyC/H6/Pv30U6Wlpam4uFjx8fHq1q2b1qxZI7vdrtTUVKWkpGjEiBE69dRTZbFYlJ+fr8svv1y//vqr9u7dK6Pxf4tkoVBIRUVFmjRpkq6//vqoaurRo0d13yYAlGK12bQhK4vwBADAMS4mwclut8tgMIRXixISEuTz+fTtt9/KaDRq3rx56tu3b4k+u3fvliRNnz5dP/zwg5544gk9++yzpca22WxRhyZJUr9+UrNm0fcDgIpyuVQ0Z45cLhfBCQCAY1xMglNeXp4efPBBTZkyRUajUUOHDtWECRPUsmVLGY3GiI/MGQwG7du3T8XFxXr88cf1z3/+U1dccYU6d+6sq6++unJFJSVJycmV6wsAAACgTqnx4BQIBGQ0GpWbm6vMzEwVFBQoKytLzZs3D1/79ttvwytMfr9fPXr00B133KGffvpJ9evX1xVXXCGj0agVK1Zo5MiR2rhxo7Kzs1W/fv2aLh8AAAAAaj44ffHFF+rXr5+MRqPeeusthUIhSZLZbNajjz6qUCikjRs36uDBg5o6dapGjx6tzp07a//+/Zo+fbouvPBCSdJbb72lxMREtW3bVrt371aLFi3UpEkTbdiwodz5vV6vvF5v+LfH46m5mwUAAABwXDKEDieZGhIMBmUwGGQwGPThhx9q+vTpmj9/vkKhkILBoM444wy9/PLL+stf/iKbzRYOVu3atZPP55PNZlPr1q112WWXKTs7u8TmENKhDSJOOeUU3XTTTWXOP2HCBE2cOLH0haFDpVatqvluAeAPdu6UXnlFq1atUteuXWu7GgAA8Ccej0cOh0Nut1uJiYnltq3xFSej0aj9+/fLarWquLhYwWBQhYWF8vl84V3z6tWrV6rf5MmTZbfbdcIJJ+hvf/ubnE6ndu3aVSo4BYNBOZ3OI84/duxYjRkzJvzb4/EoJSWl+m4QAAAAwHGvxoOT3+9X06ZNZbVaFQwG5fP51LRpUxUXF+ull17Szp07dcIJJ5To88knn+i2226TyWTSjTfeKIPBoCZNmmjmzJm64oor9Nlnn6l9+/Y68cQT9dZbb+n2228/4vwWi0UWi6WmbxMAAADAcazGg5PJZFJubq7Gjx+vDh066OOPP9a1116rSy65RAcOHFDDhg3VqFEjFRUVhfv0799fa9askdPp1CWXXKIFCxbIYrFo//79WrJkiXJycrR+/Xrt2LFDhYWFMpli+h1fAAAAAHVMjSeOUCik4cOHy2Aw6Oyzz5Yk5eTkaNCgQerfv7+6detWqs/evXu1bds2LV68WBs3bpR06JG8Tp066YUXXtCYMWM0YMAAXXDBBbriiitUqde0cnIks7lK9wYA5XK5arsCAABQTWo8OP3www/yer16+eWXNWvWLMXFxemuu+7SaaedpjvvvFMPPfSQpEPB6PA/33rrLQUCAfXr10/NmjXTqlWrZDKZlJ+fr6FDh0qS3njjDb3xxhtq1KhR5YLTvHnVdYsAcERWm63c9zABAMCxocaD07nnnqtzzz1X77//vl555RX961//ksFgUEJCggwGgwYOHChJ4S3DfT6f7r33XknSpEmTdN999+nJJ5/U6NGjlZ2dLZvNVmJ8v9+vNm3aaOXKlTr11FMrXFd6enrED+8CQFU5nU6lpqbWdhkAAKCKanw78qNNNFsOAgAAADh+RZMNjOVeBQAAAAAQnAAAAAAgEoITAAAAAERAcAIAAACACAhOAAAAABABwQkAAAAAIiA4AQAAAEAEBCcAAAAAiIDgBAAAAAAREJwAAAAAIAKCEwAAAABEQHACAAAAgAgITgAAAAAQAcEJAAAAACIgOAEAAABABKbamtjr9cpisZTbJhQKyWAwHPF6cXGx4uPjKzV/RkaG7HZ7pfoCwLHO6XQqNTW1tssAAOCYUSvBKS8vTx07dtTnn3+u9u3bH7Hd+PHjlZCQoL///e/y+/3q0KGDtm3bpqKiIlksFqWkpGjDhg1yOBxR19CjR4+q3AIAHNOsNps2ZGURngAAqKBaCU52u1033HCDXnzxRT3//PNltlm6dKmmTZum5cuXq3379srKypLFYtGGDRs0bNgwvf766+rYsWOlQpMkqV8/qVmzKtwFAByjXC4VzZkjl8tFcAIAoIJiEpwyMjLUtWtXtW7dutS1Nm3ahP+8efNmbdu2Tbm5uerbt69mzpyp1q1ba+/everevbt2796tQYMGKSkpSV9//bUcDofefvtt+Xw+XXPNNapXr17Fi0pKkpKTq+P2AAAAABznYhKc6tevL7PZrE2bNkmS5s2bpy1btuiuu+4q0c5gMMhqtapNmzZ69dVXdckll+ijjz5SQUFBqTEvvvhitW3bVi+++KIaN26sQYMGxeJWAAAAANRBMQlODRs21JQpUyRJ+fn5GjNmTPj3H/3jH/+Q2WxWQkKCBv5fe/ceFOV5v3/8AlyWVdhFWTCaAQ9DTIwpWjsYLZOqTVNt4mHSODEdpjb2EA+Jp0li6zSdGhMlrbbapq2x+UNM24jTVI3E1OigOB5gBqPSWiq2CQRErC5DWDxwEO7vH/6yP1GXR3APyr5fMzuT3YfdvZ9rPmG45nHvnTFDy5cvV1FRkfLz87Vnzx6lpKQoJiZGDz30kKqrq1VQUKBnn31WTz/9tJxO503fu7m5Wc3Nzb77Xq83OCcJAAAAoMcKSXF67LHH5PF4tGbNGjU2NurSpUtatGiRFi1aJEm6fPmybDaboqKitHHjRtXV1engwYN64403dOzYMfXu3Vv/+Mc/tG7dOh0+fFhlZWVqb29XS0uLampqlJqa6ve9c3Jy9Oqrr4biNAEAAAD0UCH5HqePP/5Yn332mY4ePaqYmBgdPnxYlZWVqqys1JEjR3Tp0iUVFhaqurpadXV1Kikp0YwZM5SSkiKHw6HHH39cffr00YwZMxQdHa3Vq1crKytLJ0+e1KlTpzR48GC/771s2TI1NDT4btXV1aE4ZQAAAAA9SEi/AHfhwoVKTEzU//73P12+fFmS9Oqrr+qpp57qsElE3759tWnTJg0bNkyStHPnTvXp00crVqzQwYMHVV1draysLOXl5cnpdCohIcHve9rtdjmdzg43AAAAAOiKkG5HvnLlSu3bt08bN27UrFmzlJGRoZKSEt+mEV9IT09Xenq6Vq9erba2Nk2aNEnJyclatWqVpk6dqoKCAs2ePVv33XffDRtMAAAAAECghbQ4DRo0SM8++6xGjBih8+fPq76+Xg888IBGjhypefPmacGCBUpMTOzwnKamJk2YMEFLly7Vc889p08//VTz58/XhQsXJEnR0d28aFZXJ8XG3uYZAcBdyOMJ9woAALjrhKQ4lZeXq6SkREePHtXu3bvV1NSkxYsXa968eYqJiVFhYaFeeeUV/frXv9aKFSt8V5Ha2tr0q1/9Sk6nU0ePHpXNZtPLL7+s9evX66OPPlJubq5+9rOfadCgQfrBD37QtUXl5wfhTAHg7hDncMjtdod7GQAA3DVCUpxqa2u1du1aTZw4Ub///e/1yCOPdLhSNGHCBB04cEB/+tOf1LdvX9/jly9f1vPPP68HH3xQs2bN0vz58xUdHa38/Hy98847Gj9+vL70pS/ppz/9qWbNmiWbzXbLa9q/f7/i4+MDep4AcLdwu91KS0sL9zIAALhrRBljTLgX0VXNzc2y2+3deq7X65XL5VJDQwMbRQAAAAARrCvdIKS76gVKd0sTAAAAAHTHXVmcAAAAACCUKE4AAAAAYIHiBAAAAAAWKE4AAAAAYIHiBAAAAAAWKE4AAAAAYIHiBAAAAAAWKE4AAAAAYIHiBAAAAAAWKE4AAAAAYIHiBAAAAAAWKE4AAAAAYIHiBAAAAAAWeoV7AeFy/PhxxcfHh3sZANBjud1upaWlhXsZAAAERMQWp/Hjx4d7CQDQo8U5HCo/eZLyBADoESK2OGnqVGnAgHCvAgB6Jo9HTVu3yuPxUJwAAD1CSIrTggULlJycrKVLlyouLk45OTk6e/asfvOb30iSbDab7r33XrW1tam5uVnnzp3r8Pw333xThw4dUl5enjZv3qy3335be/fuvb1FJSVJAwfe3msAAAAAiAgh2RwiJydHBw4c0AsvvCBJcjgccjgcvuN9+vRRZWWlSkpKFBcXd8PzbTab73GHw6HY2NhQLBsAAAAAJIXoilN8fLx27NihU6dOaf369SouLlZDQ4PWr1+vyZMnKyYmxvez0dFd63IXL15UdHR0hyJ2rebmZjU3N/vue73e7p0EAAAAgIgV9CtOra2tOnPmjBwOh5qamvTee+/p+PHjOnnypPLy8nTmzBm1t7ff8Lzm5mYZYzp97bKyMmVmZurFF1/0+zM5OTlyuVy+W2pq6m2fEwAAAIDIEvTidPDgQWVkZOjtt9/WmDFjVFBQoLlz52rmzJnav3+/srKy1NTUpIceekgTJkzwPS8rK0tJSUlyu9166aWXOrym1+vVkiVLNHHiRGVnZ+uXv/yl3/dftmyZGhoafLfq6upgnSoAAACAHiro/1Rv4sSJ2rFjh7KzszVy5EiNGTPmxkX06qUTJ07o7NmzGjt2rCTpyJEjvuNvvfWWiouLffdPnDihb37zmyovL1diYmKn72+322W32wNzMgAAAAAiUkg+4/TVr35VJ0+elN1uV3p6uqKjoxUVFaV3331X//nPf2Sz2br8esuXLw/OYgEAAADgOiH7Hqc333xTmZmZio2N1datWxUXF6dvfOMb8ng8MsZo8eLFunTpUqiWAwAAAAC3LCTF6cqVK/rtb3+rwsJCRUdHa9q0aYqKilJ7e7tOnTqlMWPGaN26dTp79qx2794diiVJdXUS25oDQHB4POFeAQAAARWS4vS3v/1No0eP1tChQ2WM0Y4dOxQXF6dHHnlEu3btUlZW1g3PqaysVFFRkfbt26e///3vevTRR2/62u3t7frrX/+q6dOn3/Q7oPzKz+/u6QAAbkGcwyG32x3uZQAAEBBBL07GGOXk5GjlypWSrl59kqTk5GQtX75cy5Yt820E0draKkkqKirS1772NT322GN64okn1K9fP9XW1kq6utnDf//7XzU0NMjlcmnfvn367ne/q4qKCt177723vK79+/crPj4+kKcKALiG2+1WWlpauJcBAEBABL04bd68WfX19Zo8ebIkKT8/X4MHD1ZbW5vy8vI0ffp0paWlqbCwUBs2bFBSUpLGjRunmpoapaSkSJLWrl2riooKSVe3KR8wYIDS09NljJHNZtOqVau6VJokadSoUXI6nYE9WQAAAAA9UtCL05NPPqn77rtPMTExkqRhw4ZJko4dOyaXy6W1a9dKkgYMGKAHH3xQq1atkiRfaZKkJUuW+P7b6XTqwIEDwV42AAAAAPhEGWNMuBcRSl6vVy6XSw0NDVxxAgAAACJYV7pBdIjWBAAAAAB3LYoTAAAAAFigOAEAAACABYoTAAAAAFigOAEAAACABYoTAAAAAFigOAEAAACABYoTAAAAAFigOAEAAACABYoTAAAAAFigOAEAAACABYoTAAAAAFjoFe4FhMvx48cVHx8f7mUAAAAAEcPtdistLS3cy+iWu6o4VVVVadGiRdqyZYtiY2Nv67XGjx8foFUBAAAAuBVxDofKT568K8tTWIpTfn6+Vq5cqeLiYhUVFSk7O1uffvpph59pb2/XlStXOhSk1NRUVVRU6A9/+IMWL17c4edbW1tls9lufRFTp0oDBtzOaQAAAAC4VR6PmrZulcfjoTjdKpvNpri4OEmSw+G46dWjkpISjR8/Xna7vUMham9v14oVK/T666/7Hrty5Yr69++v8vLyW19EUpI0cGD3TwIAAABAxLjj/qnelStXdOHCBT388MNqamoK93IAAAAAIHTFqaWlRb169VJ0tP+N/E6fPq1nnnlGTqdTH374oSSpoqJCmZmZfp/Tv39//etf//J7vLm5Wc3Nzb77Xq+3G6sHAAAAEMlCth35M888o6SkJLndbs2cObPDsZaWFr322msaPXq0xo0bp3feecd3LCYmRp9//rk8Hs8Ntw8++ECNjY2dvm9OTo5cLpfvlpqaGpTzAwAAANBzhaw4bd26VfX19fJ4PNqyZUuHYzU1Naqrq1NpaalWr14tt9v9/xfYyRWqWzm+bNkyNTQ0+G7V1dXdPwkAAAAAEemO+IzTkCFDtG7dupsea2tr6/S5xphOj9vtdtnt9u4uDQAAAADujOLUmba2NiUmJvquQl28eFFRUVHq3bu3pKufcQIAAACAYLrji9PQoUO1e/dunT59WtOmTdMLL7wgt9ut5cuX68MPP9Tjjz/evReuq5Nu80t0AQAAANwijyfcK7gtIStOtbW1KioqUmFhoXbt2qWBnXyH0nvvvadJkyYpISFB5eXlmjJliubNm6dp06b5fubcuXOaO3euxowZo9zcXMXHx3dtQfn53T0VAAAAAN0Q53B02M/gbhKS4vTZZ5/p/vvv19ixY30laPv27ZKufgappqZGZ8+e1T333KNTp05p5syZKi4u1sWLFzVjxgwtXbpUS5cu7fCaKSkpKikp0fTp0zVu3Dh98MEHGjRo0C2vaf/+/V0vWwAAAAC6ze12Ky0tLdzL6JaQFKdBgwapqqpKKSkpkqRt27b5vtx22LBh+vrXv66MjAy1t7crJiZGCxcu1MiRIzV69Gi9/vrrmjt3rmpra3Xo0CEVFRXpO9/5jqSrn2/au3evnn76aZWWlnapOI0aNUpOpzPwJwsAAACgx4kyVtvShVFLS4ti/9/nkLxer771rW9pxIgRWrlypZKTk7v1ml6vVy6XSw0NDRQnAAAAIIJ1pRvc0ZtDxF6zeYPT6dShQ4fCuBoAAAAAkSpkX4ALAAAAAHcrihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAIAFihMAAAAAWKA4AQAAAICFXuFeQKgZYyRJXq83zCsBAAAAEE5fdIIvOkJnIq441dXVSZJSU1PDvBIAAAAAd4LGxka5XK5OfybiilO/fv0kSVVVVZbhoHu8Xq9SU1NVXV0tp9MZ7uX0SGQcXOQbfGQcfGQcfGQcXOQbfGR89UpTY2OjBg4caPmzEVecoqOvfqzL5XJF7ICEitPpJOMgI+PgIt/gI+PgI+PgI+PgIt/gi/SMb/ViCptDAAAAAIAFihMAAAAAWIi44mS32/Xzn/9cdrs93Evpscg4+Mg4uMg3+Mg4+Mg4+Mg4uMg3+Mi4a6LMrey9BwAAAAARLOKuOAEAAABAV1GcAAAAAMACxQkAAAAALFCcAABARKmrq9Phw4fl8XjCvZQei4zRE0VUcTpx4oQyMzPVt29fvfzyy2JfDP8WLFigqKgo3y09PV1S5xkG41hPU1dXpyFDhqiystL3WKgz7el53yxjf/MskXFXvf/++xo6dKh69eqlhx9+WP/+978lMceB4i9fZjhw8vLylJ6erueff15paWnKy8uTxAwHkr+MmePAmzx5snJzcyUxw6EQMcWpublZU6dO1Ve+8hUdOXJEZWVlvkHDjT7++GPt3LlT9fX1qq+v17FjxzrNMBjHehqPx6MpU6Z0+IM+1Jn29LxvlrF083mWyLirPvnkE82ePVtvvPGGampqNGjQIP3whz9kjgPEX74SMxwon3/+uRYsWKADBw7o2LFj2rBhg3784x8zwwHkL2OJOQ60v/zlL/roo48k8fdEyJgIsW3bNtO3b19z8eJFY4wxx48fN1lZWWFe1Z2ptbXVJCQkmMbGxg6Pd5ZhMI71NI8++qhZt26dkWQqKiqMMaHPtKfnfbOM/c2zMWTcVfn5+Wb9+vW++3v37jWxsbHMcYD4y5cZDpyqqirz5z//2Xe/tLTUJCQkMMMB5C9j5jiw6urqTP/+/c39999vNm7cyAyHSMRccSotLdXYsWPVu3dvSVJGRobKysrCvKo70z//+U8ZYzRq1Cg5HA5NnjxZVVVVnWYYjGM9zR//+EctWrSow2OhzrSn532zjP3Ns0TGXTVlyhTNnTvXd7+8vFzp6enMcYD4y5cZDpzU1FRlZ2dLklpbW7VmzRp9+9vfZoYDyF/GzHFgvfjii3ryySc1duxYSfw9ESoRU5y8Xq+GDBniux8VFaWYmBjV19eHcVV3prKyMo0YMUKbN29WWVmZbDab5syZ02mGwTjW0wwdOvSGx0KdaU/P+2YZ+5tnKfT59yQtLS1as2aN5s+fzxwHwbX5MsOBV1paqv79+2v37t1at24dMxwE12fMHAfOvn37VFBQoF/84he+x5jh0IiY4tSrVy/Z7fYOj8XFxenSpUthWtGdKzs7W8XFxcrMzNSQIUP0u9/9Trt371Z7e7vfDDvLt7vHIkEwciPvjvzNs9frJePb8Morryg+Pl7PPfcccxwE1+bLDAdeRkaGCgoKNGLECM2ePZsZDoLrM2aOA6OpqUlz5szR+vXr5XQ6fY8zw6ERMcWpX79+On/+fIfHGhsbFRsbG6YV3T0SExPV3t6ue+65x2+GneXb3WORIBi5kXfnvpjn2tpaMu6mPXv26K233tK7774rm83GHAfY9flejxm+fVFRUfryl7+s3Nxcvf/++8xwEFyf8fVXIpjj7nnttdeUmZmpJ554osPjzHCIhPcjVqFTUFBg0tPTffcrKipMXFycuXLlShhXdWdasmSJ2bJli+/+nj17THR0tNm5c6ffDDvLt7vHeipds3FBMHIj744Z+5vnixcvknE3fPLJJyY5ObnDh7+Z48C5Wb7McOAUFBSYl156yXf/zJkzJioqymzfvp0ZDhB/GS9evJg5DoDBgwebPn36GJfLZVwul7HZbMbhcJjhw4czwyEQMcWptbXVJCcnm02bNhljjJkzZ46ZMmVKmFd1Z9q0aZNJT083+/fvNwUFBeaBBx4w3//+9zvNMBjHeipdt+NbKDONlLyvzdjfPBtDxl116dIlM3z4cPOjH/3INDY2+m4tLS3McQD4yzc3N5cZDpCamhqTkJBgNmzYYKqqqsysWbPMpEmT+F0cQP4y5ndxYFRXV5uKigrf7amnnjKrV68258+fZ4ZDIGKKkzFXt050OBwmJSXFJCUlmRMnToR7SXesn/zkJyYxMdGkpqaahQsXmgsXLhhjOs8wGMd6omv/qDcm9JlGQt7XZ+xvno0h467Ytm2bkXTDraKigjkOgM7yZYYDZ9euXWb48OEmISHBzJgxw5w7d84Yw+/iQPKXMXMceN/73vfMxo0bjTHMcChEVHEyxpjTp0+b7du3+/4nRtd1lmEwjkWCUGca6Xlfj4wDgzkOH/INDGY4vMj49jHDwRVljDGh/2QVAAAAANw9ImZXPQAAAADoLooTAAAAAFigOAEAAACABYoTAAAAAFigOAEAAACABYoTAAAAAFigOAEAAACABYoTAAAAAFigOAEAAACAhf8DpXif+17hLfAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看农产品市场所在省份\n",
    "data1 = dict(df2['农产品市场所在省份'].value_counts())\n",
    "data3 = list(data1.keys())\n",
    "data2 = list(data1.values())\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.barh(y=data3,\n",
    "         width=data2,\n",
    "         height=0.35,\n",
    "         label=\"农产品市场所在省份\",\n",
    "         edgecolor='k',\n",
    "         color='#008080')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "df7b448e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T15:42:46.512017Z",
     "start_time": "2023-08-19T15:42:46.381783Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:28.087338Z",
     "iopub.status.busy": "2023-08-25T16:10:28.087338Z",
     "iopub.status.idle": "2023-08-25T16:10:28.225642Z",
     "shell.execute_reply": "2023-08-25T16:10:28.225642Z",
     "shell.execute_reply.started": "2023-08-25T16:10:28.087338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看农产品所在的区域\n",
    "data4 = dict(df2.区域.value_counts())\n",
    "data5 = ['北方地区', '南方地区', '西北地区']\n",
    "data6 = list(data4.values())\n",
    "explodes = (0.02, 0.02, 0.02)\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.pie(data6,\n",
    "        labels=data5,\n",
    "        autopct='%1.2f%%',\n",
    "        labeldistance=1.2,\n",
    "        explode=explodes)\n",
    "plt.title(\"农产品分布区域(按中国四大地理区分布)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4e3bb4ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T15:53:58.893471Z",
     "start_time": "2023-08-19T15:53:58.705987Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:28.227068Z",
     "iopub.status.busy": "2023-08-25T16:10:28.227068Z",
     "iopub.status.idle": "2023-08-25T16:10:28.428590Z",
     "shell.execute_reply": "2023-08-25T16:10:28.428590Z",
     "shell.execute_reply.started": "2023-08-25T16:10:28.227068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看农产品类别\n",
    "data7 = dict(df2.农产品类别.value_counts())\n",
    "data8 = list(data7.keys())\n",
    "data9 = list(data7.values())\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.bar(x=data8,\n",
    "        height=data9,\n",
    "        label='数量',\n",
    "        width=0.45,\n",
    "        color='r',\n",
    "        edgecolor='white')\n",
    "plt.ylabel('出现次数', fontsize=12)\n",
    "plt.xlabel('农产品', fontsize=12)\n",
    "plt.title('农产品类别')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0b36d73d-e30e-481c-ba99-0d14878986a4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:07:48.940198Z",
     "start_time": "2023-08-19T16:07:48.449265Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:28.429589Z",
     "iopub.status.busy": "2023-08-25T16:10:28.429589Z",
     "iopub.status.idle": "2023-08-25T16:10:28.865022Z",
     "shell.execute_reply": "2023-08-25T16:10:28.865022Z",
     "shell.execute_reply.started": "2023-08-25T16:10:28.429589Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#特征工程\n",
    "df8 = pd.get_dummies(df[['农产品市场所在省份', '农产品类别']])  #对进行one_hot 编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "85cd3913-6871-4f49-8243-22f7ad74de87",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:07:49.506967Z",
     "start_time": "2023-08-19T16:07:49.186922Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:28.867023Z",
     "iopub.status.busy": "2023-08-25T16:10:28.866023Z",
     "iopub.status.idle": "2023-08-25T16:10:29.348284Z",
     "shell.execute_reply": "2023-08-25T16:10:29.348284Z",
     "shell.execute_reply.started": "2023-08-25T16:10:28.867023Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>规格</th>\n",
       "      <th>区域</th>\n",
       "      <th>low_price</th>\n",
       "      <th>avg_price</th>\n",
       "      <th>high_price</th>\n",
       "      <th>market</th>\n",
       "      <th>produce</th>\n",
       "      <th>unit</th>\n",
       "      <th>color</th>\n",
       "      <th>农产品市场所在省份_上海</th>\n",
       "      <th>...</th>\n",
       "      <th>农产品类别_永生花</th>\n",
       "      <th>农产品类别_玫瑰花</th>\n",
       "      <th>农产品类别_畜禽蛋品</th>\n",
       "      <th>农产品类别_百合花</th>\n",
       "      <th>农产品类别_粮油饲料</th>\n",
       "      <th>农产品类别_菊花</th>\n",
       "      <th>农产品类别_蔬菜</th>\n",
       "      <th>农产品类别_配叶类</th>\n",
       "      <th>农产品类别_配花类</th>\n",
       "      <th>农产品类别_鲜肉</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5127</td>\n",
       "      <td>32</td>\n",
       "      <td>1170</td>\n",
       "      <td>333</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5127</td>\n",
       "      <td>31</td>\n",
       "      <td>1170</td>\n",
       "      <td>333</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5127</td>\n",
       "      <td>32</td>\n",
       "      <td>1170</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5127</td>\n",
       "      <td>76</td>\n",
       "      <td>1170</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5127</td>\n",
       "      <td>76</td>\n",
       "      <td>1170</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951963</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30713</td>\n",
       "      <td>27696</td>\n",
       "      <td>1946841</td>\n",
       "      <td>1946841</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951964</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30713</td>\n",
       "      <td>38080</td>\n",
       "      <td>1946841</td>\n",
       "      <td>1946841</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951965</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30713</td>\n",
       "      <td>18188</td>\n",
       "      <td>1946841</td>\n",
       "      <td>1946841</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951966</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>30713</td>\n",
       "      <td>928</td>\n",
       "      <td>1946841</td>\n",
       "      <td>1946841</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951967</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>30713</td>\n",
       "      <td>815</td>\n",
       "      <td>1946841</td>\n",
       "      <td>1946841</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1951968 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           规格 区域  low_price  avg_price  high_price  market  produce     unit  \\\n",
       "0        20.0  1       19.0       22.0        24.0    5127       32     1170   \n",
       "1        20.0  1       18.0       22.0        24.0    5127       31     1170   \n",
       "2        20.0  1       16.0       19.0        22.0    5127       32     1170   \n",
       "3        20.0  1       16.0       18.0        20.0    5127       76     1170   \n",
       "4        20.0  1       20.0       24.0        26.0    5127       76     1170   \n",
       "...       ... ..        ...        ...         ...     ...      ...      ...   \n",
       "1951963   1.0  1        1.0        1.0         1.0   30713    27696  1946841   \n",
       "1951964   1.0  1        3.0        3.0         3.0   30713    38080  1946841   \n",
       "1951965   1.0  1        4.0        4.0         4.0   30713    18188  1946841   \n",
       "1951966   1.0  1        3.6        3.6         3.6   30713      928  1946841   \n",
       "1951967   1.0  1        2.2        2.2         2.2   30713      815  1946841   \n",
       "\n",
       "           color  农产品市场所在省份_上海  ...  农产品类别_永生花  农产品类别_玫瑰花  农产品类别_畜禽蛋品  \\\n",
       "0            333             0  ...          0          1           0   \n",
       "1            333             0  ...          0          1           0   \n",
       "2            216             0  ...          0          1           0   \n",
       "3             32             0  ...          0          1           0   \n",
       "4            216             0  ...          0          1           0   \n",
       "...          ...           ...  ...        ...        ...         ...   \n",
       "1951963  1946841             0  ...          0          0           0   \n",
       "1951964  1946841             0  ...          0          0           0   \n",
       "1951965  1946841             0  ...          0          0           0   \n",
       "1951966  1946841             0  ...          0          0           0   \n",
       "1951967  1946841             0  ...          0          0           0   \n",
       "\n",
       "         农产品类别_百合花  农产品类别_粮油饲料  农产品类别_菊花  农产品类别_蔬菜  农产品类别_配叶类  农产品类别_配花类  \\\n",
       "0                0           0         0         0          0          0   \n",
       "1                0           0         0         0          0          0   \n",
       "2                0           0         0         0          0          0   \n",
       "3                0           0         0         0          0          0   \n",
       "4                0           0         0         0          0          0   \n",
       "...            ...         ...       ...       ...        ...        ...   \n",
       "1951963          0           0         0         1          0          0   \n",
       "1951964          0           0         0         1          0          0   \n",
       "1951965          0           0         0         1          0          0   \n",
       "1951966          0           0         0         1          0          0   \n",
       "1951967          0           0         0         1          0          0   \n",
       "\n",
       "         农产品类别_鲜肉  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "...           ...  \n",
       "1951963         0  \n",
       "1951964         0  \n",
       "1951965         0  \n",
       "1951966         0  \n",
       "1951967         0  \n",
       "\n",
       "[1951968 rows x 50 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#从DataFrame中提取指定列\n",
    "df9 = df2[[\n",
    "    '规格', '区域', 'low_price', 'avg_price', 'high_price', 'market', 'produce',\n",
    "    'unit', 'color'\n",
    "]]\n",
    "#合并DataFrame\n",
    "df9 = pd.merge(df9, df8, left_index=True, right_index=True)\n",
    "#df9.规格.value_counts()\n",
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "331c41ed-f66b-409d-a352-89f1336b42af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-26T06:14:32.444976Z",
     "iopub.status.busy": "2023-08-26T06:14:32.444976Z",
     "iopub.status.idle": "2023-08-26T06:14:33.020605Z",
     "shell.execute_reply": "2023-08-26T06:14:33.020605Z",
     "shell.execute_reply.started": "2023-08-26T06:14:32.444976Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#热力图\n",
    "import seaborn as sns\n",
    "\n",
    "data20 = df2[[\n",
    "    '规格', '区域', 'low_price', 'avg_price', 'high_price', 'market', 'produce',\n",
    "    'unit', 'color'\n",
    "]]\n",
    "data20 = data20.corr()\n",
    "sns.heatmap(data20,\n",
    "            linewidths=0.1,\n",
    "            vmax=1.0,\n",
    "            square=True,\n",
    "            linecolor='white',\n",
    "            annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "420ba6ce-e225-458a-a344-013f8806036b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:07:50.221649Z",
     "start_time": "2023-08-19T16:07:50.161925Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:29.349702Z",
     "iopub.status.busy": "2023-08-25T16:10:29.349702Z",
     "iopub.status.idle": "2023-08-25T16:10:29.424956Z",
     "shell.execute_reply": "2023-08-25T16:10:29.424956Z",
     "shell.execute_reply.started": "2023-08-25T16:10:29.349702Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#对预测集进行处理\n",
    "df10 = pd.read_csv(r\"F:\\大三上\\数据分析与数据挖掘\\06\\小课\\数据源\\product_market.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b5941681-5213-4688-adbc-27966bf23906",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:07:51.304289Z",
     "start_time": "2023-08-19T16:07:51.288003Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:29.427384Z",
     "iopub.status.busy": "2023-08-25T16:10:29.426381Z",
     "iopub.status.idle": "2023-08-25T16:10:29.440673Z",
     "shell.execute_reply": "2023-08-25T16:10:29.440673Z",
     "shell.execute_reply.started": "2023-08-25T16:10:29.427384Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>农产品市场所在省份</th>\n",
       "      <th>市场名称映射值</th>\n",
       "      <th>农产品类别</th>\n",
       "      <th>农产品名称映射值</th>\n",
       "      <th>规格</th>\n",
       "      <th>区域</th>\n",
       "      <th>颜色</th>\n",
       "      <th>单位</th>\n",
       "      <th>数据入库时间</th>\n",
       "      <th>数据发布时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>广西</td>\n",
       "      <td>31D831D03A02090839BF1076E31DC00E</td>\n",
       "      <td>粮油饲料</td>\n",
       "      <td>0C63912C7F6775380A4AB1D78E8A3FD3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34:18.0</td>\n",
       "      <td>2016/1/1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  农产品市场所在省份                           市场名称映射值 农产品类别  \\\n",
       "0        广西  31D831D03A02090839BF1076E31DC00E  粮油饲料   \n",
       "\n",
       "                           农产品名称映射值  规格  区域  颜色  单位   数据入库时间    数据发布时间  \n",
       "0  0C63912C7F6775380A4AB1D78E8A3FD3 NaN NaN NaN NaN  34:18.0  2016/1/1  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "73e9a53b-f43d-4f89-aabc-d5b5d4c74cca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:07:56.662455Z",
     "start_time": "2023-08-19T16:07:56.643113Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:29.537061Z",
     "iopub.status.busy": "2023-08-25T16:10:29.537061Z",
     "iopub.status.idle": "2023-08-25T16:10:29.551152Z",
     "shell.execute_reply": "2023-08-25T16:10:29.551152Z",
     "shell.execute_reply.started": "2023-08-25T16:10:29.537061Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#与framing一样的处理方式，为了变成一样的列结构\n",
    "map5, map6 = df10['市场名称映射值'].value_counts(), df10['农产品名称映射值'].value_counts()\n",
    "df10['market'] = df10['市场名称映射值'].map(dict(map5))\n",
    "df10['produce'] = df10['农产品名称映射值'].map(dict(map6))\n",
    "df10.drop(['市场名称映射值', '农产品名称映射值'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "74545782-70a2-4526-9f0f-361f2f1c5f67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:07:56.815654Z",
     "start_time": "2023-08-19T16:07:56.802985Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:30.180393Z",
     "iopub.status.busy": "2023-08-25T16:10:30.178889Z",
     "iopub.status.idle": "2023-08-25T16:10:30.191111Z",
     "shell.execute_reply": "2023-08-25T16:10:30.191111Z",
     "shell.execute_reply.started": "2023-08-25T16:10:30.180393Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df10['规格'] = df10['规格'].fillna(1)\n",
    "df10['颜色'] = df10['颜色'].fillna(1)\n",
    "df10['单位'] = df10['单位'].fillna(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7a1e3139-60b4-4120-9b1d-ff42ce68f953",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:07:57.000532Z",
     "start_time": "2023-08-19T16:07:56.984361Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:30.402302Z",
     "iopub.status.busy": "2023-08-25T16:10:30.402302Z",
     "iopub.status.idle": "2023-08-25T16:10:30.424968Z",
     "shell.execute_reply": "2023-08-25T16:10:30.424968Z",
     "shell.execute_reply.started": "2023-08-25T16:10:30.402302Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "map7, map8 = df10['单位'].value_counts(), df10['颜色'].value_counts()\n",
    "df10['unit'] = df10['单位'].map(dict(map7))\n",
    "df10['color'] = df10['颜色'].map(dict(map8))\n",
    "df10.drop(['单位', '颜色'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e472aaf8-2c7e-4f0a-a423-315c072daa39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:07:57.171415Z",
     "start_time": "2023-08-19T16:07:57.153117Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:30.589681Z",
     "iopub.status.busy": "2023-08-25T16:10:30.589681Z",
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     "shell.execute_reply.started": "2023-08-25T16:10:30.589681Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df10['区域'] = df10['农产品市场所在省份']\n",
    "df10.loc[df10[\"区域\"].\n",
    "         isin(['北京', '湖北', '山东', '天津', '辽宁', '山西', '甘肃', '河南', '河北', '黑龙江']),\n",
    "         \"区域\"] = 2\n",
    "df10.loc[df10[\"区域\"].isin(\n",
    "    ['安徽', '湖南', '江西', '云南', '上海', '浙江', '江苏', '福建', '四川', '广西', '广东']),\n",
    "         \"区域\"] = 1\n",
    "df10.loc[df10[\"区域\"].isin(['新疆', '内蒙古', '宁夏', '陕西']), \"区域\"] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ca0c3be7-4a1b-47cb-9166-7d51331914d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:07:58.055228Z",
     "start_time": "2023-08-19T16:07:58.042471Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:30.794489Z",
     "iopub.status.busy": "2023-08-25T16:10:30.794489Z",
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     "shell.execute_reply.started": "2023-08-25T16:10:30.794489Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df10['low_price'] = 0\n",
    "df10['avg_price'] = 0\n",
    "df10['high_price'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6bf2ddfd-6f9e-410a-9ddf-2b93db9369b5",
   "metadata": {
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     "end_time": "2023-08-19T16:07:59.144777Z",
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     "shell.execute_reply.started": "2023-08-25T16:10:31.062081Z"
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       "  农产品市场所在省份 农产品类别   规格 区域   数据入库时间    数据发布时间  market  produce   unit  color  \\\n",
       "0        广西  粮油饲料  1.0  1  34:18.0  2016/1/1    1456       26  36661  36661   \n",
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       "  农产品市场所在省份 农产品类别    规格 区域               数据入库时间      数据发布时间  market  produce  \\\n",
       "0        云南   玫瑰花  20.0  1  2016-07-18 15:05:19  2015-11-27    5127       32   \n",
       "\n",
       "   unit  color  low_price  avg_price  high_price  \n",
       "0  1170    333       19.0       22.0        24.0  "
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    "df2.head(1)"
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  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b2b6d21b-95cc-4eda-befc-6a608c61e5dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:08:04.312397Z",
     "start_time": "2023-08-19T16:08:03.812729Z"
    },
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     "shell.execute_reply.started": "2023-08-25T16:10:31.423436Z"
    },
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   },
   "outputs": [],
   "source": [
    "df11 = pd.get_dummies(df[['农产品市场所在省份', '农产品类别']])  #对进行one_hot 编码\n",
    "df12 = df10[[\n",
    "    '规格', '区域', 'low_price', 'avg_price', 'high_price', 'market', 'produce',\n",
    "    'unit', 'color'\n",
    "]]  #从DataFrame中提取指定列\n",
    "#合并DataFrame\n",
    "df12 = pd.merge(df12, df11, left_index=True, right_index=True)\n",
    "#df12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0ae6627e-dd5a-4745-b52a-ebd2f7231d94",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:08:08.170964Z",
     "start_time": "2023-08-19T16:08:07.877787Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:31.875149Z",
     "iopub.status.busy": "2023-08-25T16:10:31.875149Z",
     "iopub.status.idle": "2023-08-25T16:10:31.889549Z",
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     "shell.execute_reply.started": "2023-08-25T16:10:31.875149Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#导包\n",
    "from sklearn.model_selection import train_test_split  #切分数据集\n",
    "from sklearn.ensemble import RandomForestRegressor  #RFC\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "acdadde6-2440-402d-8705-b43099d86a12",
   "metadata": {
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     "shell.execute_reply.started": "2023-08-25T16:10:31.890593Z"
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36658</th>\n",
       "      <td>36661</td>\n",
       "      <td>3712</td>\n",
       "      <td>29</td>\n",
       "      <td>36661</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36659</th>\n",
       "      <td>36661</td>\n",
       "      <td>3712</td>\n",
       "      <td>29</td>\n",
       "      <td>36661</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36660</th>\n",
       "      <td>36661</td>\n",
       "      <td>3712</td>\n",
       "      <td>29</td>\n",
       "      <td>36661</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36661 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       color  market  produce   unit  农产品市场所在省份_上海  农产品市场所在省份_云南  \\\n",
       "0      36661    1456       26  36661             0             1   \n",
       "1      36661    1456       26  36661             0             1   \n",
       "2      36661    1456       26  36661             0             1   \n",
       "3      36661    1456       26  36661             0             1   \n",
       "4      36661    1456       26  36661             0             1   \n",
       "...      ...     ...      ...    ...           ...           ...   \n",
       "36656  36661    3712       29  36661             0             0   \n",
       "36657  36661    3712       29  36661             0             0   \n",
       "36658  36661    3712       29  36661             0             0   \n",
       "36659  36661    3712       29  36661             0             0   \n",
       "36660  36661    3712       29  36661             0             0   \n",
       "\n",
       "       农产品市场所在省份_内蒙古  农产品市场所在省份_北京  农产品市场所在省份_四川  农产品市场所在省份_天津  ...  \\\n",
       "0                  0             0             0             0  ...   \n",
       "1                  0             0             0             0  ...   \n",
       "2                  0             0             0             0  ...   \n",
       "3                  0             0             0             0  ...   \n",
       "4                  0             0             0             0  ...   \n",
       "...              ...           ...           ...           ...  ...   \n",
       "36656              0             1             0             0  ...   \n",
       "36657              0             1             0             0  ...   \n",
       "36658              0             1             0             0  ...   \n",
       "36659              0             1             0             0  ...   \n",
       "36660              0             1             0             0  ...   \n",
       "\n",
       "       农产品类别_畜禽蛋品  农产品类别_百合花  农产品类别_粮油饲料  农产品类别_菊花  农产品类别_蔬菜  农产品类别_配叶类  \\\n",
       "0               0          0           0         0         0          0   \n",
       "1               0          0           0         0         0          0   \n",
       "2               0          0           0         0         0          0   \n",
       "3               0          0           0         0         0          0   \n",
       "4               0          0           0         0         0          0   \n",
       "...           ...        ...         ...       ...       ...        ...   \n",
       "36656           1          0           0         0         0          0   \n",
       "36657           1          0           0         0         0          0   \n",
       "36658           1          0           0         0         0          0   \n",
       "36659           1          0           0         0         0          0   \n",
       "36660           1          0           0         0         0          0   \n",
       "\n",
       "       农产品类别_配花类  农产品类别_鲜肉  区域   规格  \n",
       "0              0         0   1  1.0  \n",
       "1              0         0   1  1.0  \n",
       "2              0         0   1  1.0  \n",
       "3              0         0   1  1.0  \n",
       "4              0         0   1  1.0  \n",
       "...          ...       ...  ..  ...  \n",
       "36656          0         0   1  1.0  \n",
       "36657          0         0   1  1.0  \n",
       "36658          0         0   1  1.0  \n",
       "36659          0         0   1  1.0  \n",
       "36660          0         0   1  1.0  \n",
       "\n",
       "[36661 rows x 47 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据准备\n",
    "X = df9[df9.columns.difference(['low_price', 'avg_price', 'high_price'])]\n",
    "Y = df9[['low_price', 'avg_price', 'high_price']]\n",
    "Z = df12[df12.columns.difference(['low_price', 'avg_price', 'high_price'])]\n",
    "Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3a418014-9df9-4b96-9e34-5ad96022fe04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:08:24.575240Z",
     "start_time": "2023-08-19T16:08:23.724060Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:32.047515Z",
     "iopub.status.busy": "2023-08-25T16:10:32.047515Z",
     "iopub.status.idle": "2023-08-25T16:10:32.918154Z",
     "shell.execute_reply": "2023-08-25T16:10:32.918154Z",
     "shell.execute_reply.started": "2023-08-25T16:10:32.047515Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#拆分数据集\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,\n",
    "                                                    Y,\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=536)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2e574de8-42ee-4b02-87e3-7c1b5196e4dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:33:13.035770Z",
     "start_time": "2023-08-19T16:08:27.158208Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:10:33.107524Z",
     "iopub.status.busy": "2023-08-25T16:10:33.106405Z",
     "iopub.status.idle": "2023-08-25T16:28:57.854020Z",
     "shell.execute_reply": "2023-08-25T16:28:57.854020Z",
     "shell.execute_reply.started": "2023-08-25T16:10:33.107524Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(n_estimators=321, max_depth=25)  #建立模型\n",
    "rf.fit(X_train, Y_train)  #模型训练\n",
    "pred = rf.predict(X_test)  #拿训练完的模型对测试集进行预测\n",
    "score_f = rf.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3a3136b2-c493-4902-a8d7-62bb4f266009",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:33:13.082656Z",
     "start_time": "2023-08-19T16:33:13.036770Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:28:57.856022Z",
     "iopub.status.busy": "2023-08-25T16:28:57.856022Z",
     "iopub.status.idle": "2023-08-25T16:28:57.901342Z",
     "shell.execute_reply": "2023-08-25T16:28:57.901342Z",
     "shell.execute_reply.started": "2023-08-25T16:28:57.856022Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 46.637614857416914\n",
      "MAE 1.8907408272005262\n",
      "Random Forest:0.803577432703927\n"
     ]
    }
   ],
   "source": [
    "#模型评估\n",
    "print('MSE', mean_squared_error(Y_test, pred))\n",
    "print('MAE', mean_absolute_error(Y_test, pred))\n",
    "print('Random Forest:{}'.format(score_f))\n",
    "#print(\"RF准确率为:{0:%}\".format(metrics.accuracy_score(ss, pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ef59abd1-17c6-42df-b61c-c08690699fa0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:33:37.011725Z",
     "start_time": "2023-08-19T16:33:36.447929Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-25T16:30:19.682657Z",
     "iopub.status.busy": "2023-08-25T16:30:19.682657Z",
     "iopub.status.idle": "2023-08-25T16:30:20.242444Z",
     "shell.execute_reply": "2023-08-25T16:30:20.242444Z",
     "shell.execute_reply.started": "2023-08-25T16:30:19.682657Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17.99884413  9.68284497 23.16834881]\n",
      " [17.99884413  9.68284497 23.16834881]\n",
      " [17.99884413  9.68284497 23.16834881]\n",
      " ...\n",
      " [18.90733605 18.93951697 23.91273523]\n",
      " [18.90733605 18.93951697 23.91273523]\n",
      " [18.90733605 18.93951697 23.91273523]]\n"
     ]
    }
   ],
   "source": [
    "#ss = Y_test.values\n",
    "pred1 = rf.predict(Z)\n",
    "#pred1\n",
    "print(pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "939e93bb-f0d6-475e-a153-f6ee73410a64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:34:25.794381Z",
     "start_time": "2023-08-19T16:34:25.729175Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-19T08:21:37.412197Z",
     "iopub.status.busy": "2023-08-19T08:21:37.412197Z",
     "iopub.status.idle": "2023-08-19T08:21:37.471848Z",
     "shell.execute_reply": "2023-08-19T08:21:37.471848Z",
     "shell.execute_reply.started": "2023-08-19T08:21:37.412197Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred2 = pd.DataFrame(pred1)\n",
    "df13 = pd.read_csv(r\"F:\\大三上\\数据分析与数据挖掘\\06\\小课\\数据源\\product_market.csv\",\n",
    "                   low_memory=False,\n",
    "                   encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "673fe5fe-2aa0-4b5d-8abb-89904865aa5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:34:28.505938Z",
     "start_time": "2023-08-19T16:34:28.385838Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-19T08:24:34.490330Z",
     "iopub.status.busy": "2023-08-19T08:24:34.490330Z",
     "iopub.status.idle": "2023-08-19T08:24:34.608961Z",
     "shell.execute_reply": "2023-08-19T08:24:34.608961Z",
     "shell.execute_reply.started": "2023-08-19T08:24:34.490330Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred2.to_csv(r\"F:\\大三上\\数据分析与数据挖掘\\06\\小课\\数据源\\预测.csv\",\n",
    "             mode='w',\n",
    "             header=False,\n",
    "             index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4b3df7d0-a880-47e9-9fa7-aaf1a89111ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:34:29.490738Z",
     "start_time": "2023-08-19T16:34:29.464645Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-19T08:28:25.001260Z",
     "iopub.status.busy": "2023-08-19T08:28:25.001260Z",
     "iopub.status.idle": "2023-08-19T08:28:25.020688Z",
     "shell.execute_reply": "2023-08-19T08:28:25.020688Z",
     "shell.execute_reply.started": "2023-08-19T08:28:25.001260Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>农产品市场所在省份</th>\n",
       "      <th>市场名称映射值</th>\n",
       "      <th>农产品类别</th>\n",
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       "      <th>数据入库时间</th>\n",
       "      <th>数据发布时间</th>\n",
       "      <th>low_price</th>\n",
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       "      <td>广西</td>\n",
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       "      <td>9.682845</td>\n",
       "      <td>23.168349</td>\n",
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       "      <th>2</th>\n",
       "      <td>广西</td>\n",
       "      <td>31D831D03A02090839BF1076E31DC00E</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34:18.0</td>\n",
       "      <td>2016/1/4</td>\n",
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       "      <td>9.682845</td>\n",
       "      <td>23.168349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>广西</td>\n",
       "      <td>31D831D03A02090839BF1076E31DC00E</td>\n",
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       "      <td>2016/1/6</td>\n",
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       "      <td>云南</td>\n",
       "      <td>3013B031B1CE72FE1109A1ECFF9A96D9</td>\n",
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       "      <th>36657</th>\n",
       "      <td>云南</td>\n",
       "      <td>3013B031B1CE72FE1109A1ECFF9A96D9</td>\n",
       "      <td>配叶类</td>\n",
       "      <td>99DDF632CCD28F53A1A27D0BB45EFDF0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34:18.0</td>\n",
       "      <td>2016/1/26</td>\n",
       "      <td>18.907336</td>\n",
       "      <td>18.939517</td>\n",
       "      <td>23.912735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36658</th>\n",
       "      <td>云南</td>\n",
       "      <td>3013B031B1CE72FE1109A1ECFF9A96D9</td>\n",
       "      <td>配叶类</td>\n",
       "      <td>99DDF632CCD28F53A1A27D0BB45EFDF0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34:18.0</td>\n",
       "      <td>2016/1/27</td>\n",
       "      <td>18.907336</td>\n",
       "      <td>18.939517</td>\n",
       "      <td>23.912735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36659</th>\n",
       "      <td>云南</td>\n",
       "      <td>3013B031B1CE72FE1109A1ECFF9A96D9</td>\n",
       "      <td>配叶类</td>\n",
       "      <td>99DDF632CCD28F53A1A27D0BB45EFDF0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34:18.0</td>\n",
       "      <td>2016/1/28</td>\n",
       "      <td>18.907336</td>\n",
       "      <td>18.939517</td>\n",
       "      <td>23.912735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36660</th>\n",
       "      <td>云南</td>\n",
       "      <td>3013B031B1CE72FE1109A1ECFF9A96D9</td>\n",
       "      <td>配叶类</td>\n",
       "      <td>99DDF632CCD28F53A1A27D0BB45EFDF0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34:18.0</td>\n",
       "      <td>2016/1/29</td>\n",
       "      <td>18.907336</td>\n",
       "      <td>18.939517</td>\n",
       "      <td>23.912735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36661 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      农产品市场所在省份                           市场名称映射值 农产品类别  \\\n",
       "0            广西  31D831D03A02090839BF1076E31DC00E  粮油饲料   \n",
       "1            广西  31D831D03A02090839BF1076E31DC00E  粮油饲料   \n",
       "2            广西  31D831D03A02090839BF1076E31DC00E  粮油饲料   \n",
       "3            广西  31D831D03A02090839BF1076E31DC00E  粮油饲料   \n",
       "4            广西  31D831D03A02090839BF1076E31DC00E  粮油饲料   \n",
       "...         ...                               ...   ...   \n",
       "36656        云南  3013B031B1CE72FE1109A1ECFF9A96D9   配叶类   \n",
       "36657        云南  3013B031B1CE72FE1109A1ECFF9A96D9   配叶类   \n",
       "36658        云南  3013B031B1CE72FE1109A1ECFF9A96D9   配叶类   \n",
       "36659        云南  3013B031B1CE72FE1109A1ECFF9A96D9   配叶类   \n",
       "36660        云南  3013B031B1CE72FE1109A1ECFF9A96D9   配叶类   \n",
       "\n",
       "                               农产品名称映射值  规格  区域  颜色  单位   数据入库时间     数据发布时间  \\\n",
       "0      0C63912C7F6775380A4AB1D78E8A3FD3 NaN NaN NaN NaN  34:18.0   2016/1/1   \n",
       "1      0C63912C7F6775380A4AB1D78E8A3FD3 NaN NaN NaN NaN  34:18.0   2016/1/2   \n",
       "2      0C63912C7F6775380A4AB1D78E8A3FD3 NaN NaN NaN NaN  34:18.0   2016/1/3   \n",
       "3      0C63912C7F6775380A4AB1D78E8A3FD3 NaN NaN NaN NaN  34:18.0   2016/1/4   \n",
       "4      0C63912C7F6775380A4AB1D78E8A3FD3 NaN NaN NaN NaN  34:18.0   2016/1/6   \n",
       "...                                 ...  ..  ..  ..  ..      ...        ...   \n",
       "36656  99DDF632CCD28F53A1A27D0BB45EFDF0 NaN NaN NaN NaN  34:18.0  2016/1/25   \n",
       "36657  99DDF632CCD28F53A1A27D0BB45EFDF0 NaN NaN NaN NaN  34:18.0  2016/1/26   \n",
       "36658  99DDF632CCD28F53A1A27D0BB45EFDF0 NaN NaN NaN NaN  34:18.0  2016/1/27   \n",
       "36659  99DDF632CCD28F53A1A27D0BB45EFDF0 NaN NaN NaN NaN  34:18.0  2016/1/28   \n",
       "36660  99DDF632CCD28F53A1A27D0BB45EFDF0 NaN NaN NaN NaN  34:18.0  2016/1/29   \n",
       "\n",
       "       low_price  avg_price  high_price  \n",
       "0      17.998844   9.682845   23.168349  \n",
       "1      17.998844   9.682845   23.168349  \n",
       "2      17.998844   9.682845   23.168349  \n",
       "3      17.998844   9.682845   23.168349  \n",
       "4      17.998844   9.682845   23.168349  \n",
       "...          ...        ...         ...  \n",
       "36656  18.907336  18.939517   23.912735  \n",
       "36657  18.907336  18.939517   23.912735  \n",
       "36658  18.907336  18.939517   23.912735  \n",
       "36659  18.907336  18.939517   23.912735  \n",
       "36660  18.907336  18.939517   23.912735  \n",
       "\n",
       "[36661 rows x 13 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred2.columns = ['low_price', 'avg_price', 'high_price']\n",
    "#合并\n",
    "df14 = pd.merge(df13, pred2, left_index=True, right_index=True)\n",
    "df14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "602cd64a-8c3a-4232-a633-2e68cfdba78c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:36:01.065937Z",
     "start_time": "2023-08-19T16:36:00.838243Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-19T08:31:31.033409Z",
     "iopub.status.busy": "2023-08-19T08:31:31.033409Z",
     "iopub.status.idle": "2023-08-19T08:31:31.265644Z",
     "shell.execute_reply": "2023-08-19T08:31:31.265644Z",
     "shell.execute_reply.started": "2023-08-19T08:31:31.033409Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df14.to_csv(r\"F:\\大三上\\数据分析与数据挖掘\\06\\小课\\数据源\\product1.csv\", mode='w', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "e5041fb2-1a0e-4d8d-a58a-9d282f571c84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:36:02.613270Z",
     "start_time": "2023-08-19T16:36:02.603651Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-19T08:31:35.219710Z",
     "iopub.status.busy": "2023-08-19T08:31:35.219710Z",
     "iopub.status.idle": "2023-08-19T08:31:35.292061Z",
     "shell.execute_reply": "2023-08-19T08:31:35.292061Z",
     "shell.execute_reply.started": "2023-08-19T08:31:35.219710Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df15 = pd.read_csv(r\"C:\\Users\\26931\\Desktop\\product1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "ee6ffef8-7d3d-44eb-ac22-8a3ec0d9582c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-19T16:36:02.889610Z",
     "start_time": "2023-08-19T16:36:02.884007Z"
    },
    "execution": {
     "iopub.execute_input": "2023-08-19T08:31:35.474359Z",
     "iopub.status.busy": "2023-08-19T08:31:35.474359Z",
     "iopub.status.idle": "2023-08-19T08:31:35.500082Z",
     "shell.execute_reply": "2023-08-19T08:31:35.500082Z",
     "shell.execute_reply.started": "2023-08-19T08:31:35.474359Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "500790cb-7379-4bbc-a8c1-d40b078455ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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